{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "985226b8",
   "metadata": {},
   "source": [
    "# statsmodels\n",
    "1. statsmodels.tsa.stattools acf pacf\n",
    "3. statsmodels.tsa.arima.model ARIMA\n",
    "2. statsmodels.stats.diagnostic acorr_ljungbox\n",
    "\n",
    "# time series plot - China GDP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "30599566",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "plt.rcParams['axes.unicode_minus']=False\n",
    "plt.rcParams['font.family']=\"simHei\"\n",
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9e4ef9b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "1953     824.4\n",
       "1954     859.8\n",
       "1955     911.6\n",
       "1956    1030.7\n",
       "1957    1071.4\n",
       "Name: GDP, dtype: float64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GDP = pd.read_csv('./data/ChinaGDP.csv',index_col=0).squeeze(\"columns\")\n",
    "GDP.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9f45ce1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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3bl199dW65ppr6n3elStX6q233pIk3X777RoyZIjb/T755BN9+eWXOu+885wFwWBR4POMi0z0mT7TZ/pMn+kzfW7MdhurkHMmFohC1ecz7bmIjLRp4cJ8tWljldVqUHW1VF0tl8fvvx+n995L8LtNh0svrVBmZqWqqgynC2Q6/dheOLNYpC1bTNq+vWFmHmmpDAabkpKsSkiwqVWr2p/tj+fNi9WBA4EX4nyNWmvMYllzzKADa+UhnLXkazLAmYAMAqFFBv1Dga+WZ555Rrt27VK7du00c+ZMj/v9+9//1pIlS5SZmak77rij3uf97LPP9Pnnn0uSZsyYoZSUFLf7rVu3Tq+++qratm2rWbNm1eucFPjca45vcOkzfabP9Jk+0+dw7nP79lbZbPL4kZdn1PDhHWSxBFZsWbYsT+3bW2Vwc5jBYF97acSIlIDb/fbbPLVvb3P2z2qt2+e3307Q668HXsi58cZSXXttmaxWR7sG52OrVfrkk1jNnRsfcLtXXFGuSy+tkNXq2p69//bvLVsWre++C/wK+YABlerXr8pZyPqlXam62qDt2yP13/8GXng6+2yLOneudnmOrVaD8+ujR406dizwQkurVlbFx9tcnoea7VqtUmWlZLG0jJFlzVFMjFVpaRbFxPwyJaVjJFx0tE3LlkUrJ6fhi3BS4xXiGrtY1txukqiNtfIQjlrqNRngTEEGgdAig/6pb4Ev8HdFZ7ji4mJJUocOHbzu16pVK0nS8ePHG/S8ERERat++vc/zFhYWymKxKDIy7P4JQi472/3dn95UVRn08ssJuv56e4jc1Ev1/vvBtfvSSwn64x/L6rRZ8+sPPgiu7ZkzWykr65c+O9p0fPZ0J6yvdqdPb6Vx48rd9tXhk09ig2r7739P1FVXldfoq8Hlgum8eTFBtfvXv7bW6NEVdfrsaF+SFi2KDqrtyZNba+RIz+uTfPddcO0+/HBrjR5dKYPBJoNBbj++/jq452PatERdeWWF86K3weD6j/jll8H9+/3tb/Z/P8n+3No//7JPff4NH3ustcaMqXD5fXC0abMZgv73u+++Nho6tNJ5cbf2BXtJWr06Kqi2J05sq/79q9zmT5J+/NEUVLu33NJW/fpVSVKNf0PX/TZtCq7t229vq4suqvJQELFpw4bgnotbb22r9PSqGs+x6/Nts0lbtwbX5z/8IUndu1skuS8CHDgQEVS7V1zRXqmp9ouGrr9vvzzOzTUG1fallyarXTvXtms+PnEiuHaHD09RfLzNpa+1P8rKDEG1PWhQikwm9/2VJIvFEFChzNFuenpqQMf4y2IxaOhQ73/vBdvuqFEN367De+/F6733Ai/g+bJgQawWLPBzbtEAbdwYrY0bG369s59/jtTPPzf838LFxUad/tMcbtQslMXE2JSfb1RZWeDFzm7dqnTrraWKirIXoqKibIqMdDy2f37qqURt3hx48XfwYHOjjIbLzDT73Gf8+DLNmtUq4ELc+PHe34ynpFiVlVUWULEsK8v/4tbUqUXaty/Sj+kuDcrMrNS0af4V4Rqr3dqSk62aNKlEkyYFdTgAAADQIoXdCL7JkycrPz9fgwcP1v/+7/963O+bb77RBx98oPbt2+v555+v93nffvttrVixQomJiXrppZc87vfzzz/riSeekCQ9//zzXouBvjCCz73x49tp+XLmbwEAAEDjioiwKSJCMhrtnysqDKquDqwQLknt2lUrI8OsqCibTCZ7wcjx2fG9//f/YnXwYP3WcIuOrnvDSGNOHcloOFeNveZcY013yTSaQHBa6jUZ4ExBBoHQIoP+YQSfByaTyet2x8i5ykrPo3Ia87yBnNvdWn1RUVGaMWOGJHksEjrO5QhTS1FZGRHqLgAAALhlMtnUpo1kNLr/OHZMKi8PvEDUpo1N6ek2t20aDPbPa9YYlJ8feNtduth0zTVWZ1v2gtYvH9nZRu3bF3i7vXtbdd99Vueo9Zp9NRikGTOM2rYt8JFlAwZY9cIL1lrPg83ZttEo3XVXhNasCbztUaOsmjev2vkc1PbMM0ZNmRL436L33is9/LD3t2bt2hk0ZUrATeuKKyKVlub5psI//1maNcsWcLHsz3+OVXKy91GjjdV2crL0pz/Z9PbbBvlbhLvxRqvOPz/Jrz68+qp06JBVy5f7+h0xaMQIq157LUoxMf695/rqK+kvf6nWe++5H8ltMtl0441WzZxp9LvNmv71L+nZZy3617+MWrHCPpo1IcGmESNsuvlmqzp0iJJUv3aXLzeouFhq1Ur1bhcIZy31mgxwpiCDQGiRwaYRdgW+iAj7G+qoKO9TwTj2q6qqatLz1izwNdS54er0LKgBMxrt0wk51L67ubLSPiVcoCIibIqNdd+m4+vSUgV1t7fJZFPr1q5T+NV8fOKEZDYH3m5MjE21l5Gs3ffcXPtd6oGKi7Opa1f301EajdJPP0nFxcFd2LzgAlud58Dx2WCQfvzRoJMnA287KcmmQYM8D3Zev96ggoLA223b1qZ+/byv8bR9u0GnTgXedkKCTeeea3/sbrq9Awek0tLA242P/6Vdyf30kfv2SSUlgbfdurVNffp4nq508+bg/v2Sk20aPtxW53et5tdLlhh07FjgbXfubNNvfmN1aUv65fFnnxl16FDg7Z59tv1ieu1pP2t+/vzz4Nru3NmmK6+sO3rB0fb8+UYdORJcAeC3v7W6/beT7J8//tiogwcDbzstzaZbb7XWufjv+Pzaa0bt2hV4u336WPXgg9Y6/241+/3MM0Zt3Rp4EaBfP6umTnVt28FgkB5/3KhNm4IrXDz3nPvn2fFx771GrV8feNsZGVa9+mq12/5K0p13BlcQGTrUqn/9q9prn6+/PkIrVgTe9ogRVv3nP9V1vu/4fR4/PsKPC/R1jRxp1SefVLv9/8LxvRkzjHryycALOY8/btXDD3seQRRsgej++723W5+2J0703nZUlIJqd/x4m266yfPr6/79Nm3bFnCzGjvWpiFDvE9S8utf27RmTeBtjxplH03nyc03W/X3vwc2Ba/JZC+Q+NJYbaemBlcs87EiQqO3PXNmtfbskd9FuJkz/V/HLTZW+vLLav3lLzY/CnHWgEatxcZKr75q1ZQpVi/FMv/bc6dDB+nhh6167DH7c2Ox1P1/sj7tPvxwgzQHAAAAoJ7CrsAXc/rdlcVi8bpfdbX9TY7Z7HsdhoY8b83t/p776aef9rr9+PHjTNFZw0UXJWjRosCnApo8udjrVEDBTjH0wAPe261P2/ff3zh9vueexuvz3Xc3Tp/vuKPx+nzbbY3T59tvb7w+33VX4/T5z39uvD7feWfj9PmWWxqvzxMmeG87Jia4dseP993nuLjG6XObNsG1+8c/+u5zRERwbY8bV6w//clz2zk5wbV71VUlGj3ae583bUrQ1q2Bt3355SUaNMhz25ddlqBNmwJvd/ToEvXs6b3Po0YlaP36wNv+1a9K1KGD57ZHjEjQmjWBtztsWIni4733OTMzQStWBLOeVolsNs9tZ2QkaPnywNu9+OISVVR47/PYsUY99VTgUwRefXW+8vM9Fxquvtqov/+94dttzLabW7uN2XZEhJSVFdj0jllZZTIai+TrT/bGbPuRR6Tt2/2ZOtK+ztojjxT4bLMp2n7nHf+njiwuVsBrM06dKv35z0ZlZ8dp9eoolZQYlZBgVWamWePH29fHC6ZdyX6jwK232j9qa6i3by31/SBwpiCDQGiRQSC0yKB/ak7RGYzAb2k+wyUkJEiyF728KSmxX7Qxuptbpx7nPXHihKxWz2/6S0tLnY8b6txwNX58mUymwJaWNJlsGj/e+xy3jdVuY7ZNn5umbfrcNG3T56Zpmz43Tdv0uWnabsw+p6RYlZXl2M/XOezbs7LKfK7/1VjtNmbbza3dxm576tQiZWZWynsBTnIUtKZNK/LZZmO3HRsrzZ5doAkTSj1mxmSyacKE0oDXhWvstmfMKNL69bl66KFTGjGiQhddZNaIERV66KFTWr8+VzNm1G9duORkqyZNKlF29gl9+eVxZWef0KRJJX79LgAAAABAYwq7ClNSkn1dBV8FvqIi+5vdmAZaBdxx3urqap08edLjfoWFhc7HDXVuuOIiU+O325ht0+emaZs+N03b9Llp2qbPTdM2fa6rsYotzbFA1Nzabcy2KZY1bdsSRTgAAAAALZOhtLQ0sNuaz3Dfffed3nvvPRkMBv3jH/9wjqyrbcqUKTp48KC6du2qqVOn1vu8R48e1SOPPCJJmjRpki666CK3+2VnZ+vrr7+WJL366quKi4sL+pyOY48ePcoUnbWUl0s33ODPVEBSZmal3xdAGqvdxmybPjdN2/S5adqmz03TNn1umrbpc9O03Zh9drTv7xSBZ0K7zbHPzfG5cMjP9z69Y300ZtsIDy35/SBwJiCDQGiRQSC0yKB/ak7RWVbmezahOseHW4EvNzdXDz30kCTptttu07Bhw+rsU1paqrvvvltWq1VDhw7VxIkTG+Tc9913n06ePKlhw4bptttuc7vPk08+qQMHDigpKUkvvPBCvc5Hgc87LjLRZ/pMn+kzfabP9Lmx++zQWMWW5lggcrS7cWOCioul6Ghzi30ugFBq6e8HgVAjg0BokUEgtMigfyjwueEYnde5c2dNnTpVERERLtsXLlyoDz/8UJLnImAwHKPzTCaTnnnmGbVv395l+4EDB/Tkk09KktcioL8o8PmHC270OVR9bugLm03R5+b4PNNn+uypXTJIn5uyz6iLv0WB0CKDQGiRQSC0yCAQWmTQPxT43Fi2bJn+9a9/SZLGjBmjCRMmOLf9/PPPeuaZZ1ReXq6YmBjNmjVLsbGxzu179uyR2WxWVFSUevToEdB5jx07pkceeUQ2m009evTQAw88oOho+3RQpaWlevbZZ3Xo0CFJ0kMPPaTevXvX6+ekwAec2cggEFpkEAgtMgiEFhkEQosMAqFFBoHQIoP+qW+BL7KhO3QmGDZ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      "text/plain": [
       "<Figure size 1800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(1,1,figsize=(12,4),dpi=150,facecolor='whitesmoke')\n",
    "ax.plot(GDP,marker='o',ls='-',lw=1.5,color='blue')\n",
    "ax.set_xlabel(xlabel='years',fontsize=17)\n",
    "ax.set_ylabel(ylabel='GDP(10 thousands)',fontsize=15)\n",
    "plt.xticks(fontsize=15)\n",
    "plt.yticks(fontsize=15)\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5a194146",
   "metadata": {},
   "outputs": [],
   "source": [
    "GDPy = GDP[1::]\n",
    "GDPx = GDP[:-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee6a5339",
   "metadata": {},
   "source": [
    "# stability test - sample:bjch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fb8a9f4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>CCA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018/10</td>\n",
       "      <td>5541.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018/11</td>\n",
       "      <td>5689.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018/12</td>\n",
       "      <td>5877.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019/01</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019/02</td>\n",
       "      <td>5000.63</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Time      CCA\n",
       "0  2018/10  5541.56\n",
       "1  2018/11  5689.42\n",
       "2  2018/12  5877.06\n",
       "3  2019/01      NaN\n",
       "4  2019/02  5000.63"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "import matplotlib.ticker as ticker\n",
    "\n",
    "bjch = pd.read_csv('./data/bjch.csv',encoding='utf-8')\n",
    "bjch.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cda75bef",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/zh/fmxxp3g1653ccyjmyp5z3bc00000gn/T/ipykernel_69524/4220849784.py:2: FutureWarning: Series.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.\n",
      "  bjch[f] = bjch[f].interpolate()\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>CCA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018/10</td>\n",
       "      <td>5541.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018/11</td>\n",
       "      <td>5689.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018/12</td>\n",
       "      <td>5877.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019/02</td>\n",
       "      <td>5000.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2019/03</td>\n",
       "      <td>5121.03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Time      CCA\n",
       "0  2018/10  5541.56\n",
       "1  2018/11  5689.42\n",
       "2  2018/12  5877.06\n",
       "4  2019/02  5000.63\n",
       "5  2019/03  5121.03"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for f in bjch:\n",
    "    bjch[f] = bjch[f].interpolate()\n",
    "    bjch.dropna(inplace=True)\n",
    "bjch.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2fe1db06",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xlabs = bjch.Time\n",
    "yvalue = bjch.CCA\n",
    "ticker_spacing = 5\n",
    "\n",
    "fig,ax = plt.subplots(1,1,figsize=(12,4),dpi=150,facecolor='whitesmoke')\n",
    "\n",
    "\n",
    "ax.plot(xlabs,yvalue,color='b',marker='o')\n",
    "ax.xaxis.set_major_locator(ticker.MultipleLocator(ticker_spacing))\n",
    "ax.set_xlabel(xlabel='time',fontsize=15)\n",
    "ax.set_ylabel(ylabel='accumulative construction area', fontsize=15)\n",
    "ax.vlines(x=['2020/04','2020/09'],ymin=5230,ymax=6250,color='g',ls='--',lw=1.5)\n",
    "ax.hlines(y=[5250,6250],xmin='2020/04',xmax='2020/09',color='g',ls='--',lw=1.5)\n",
    "\n",
    "\n",
    "plt.xticks(fontsize=15)\n",
    "plt.yticks(fontsize=15)\n",
    "plt.suptitle(\"demo\",fontsize=18)\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9bffd8db",
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.tsa.stattools import acf\n",
    "\n",
    "def ACF(ts, lag=20, fname=\"\"):\n",
    "    lag_acf = acf(ts, nlags=lag, fft=False)\n",
    "    plt.vlines(x=list(range(lag+1)),ymin=np.zeros(lag+1),ymax=lag_acf,lw=2.0,color='black')\n",
    "    plt.axhline(y=0,ls=':',color='blue')\n",
    "    plt.axhline(y=-1.96/np.sqrt(len(ts)),ls='--',color='red')\n",
    "    plt.axhline(y=1.96/np.sqrt(len(ts)),ls='--',color='red')\n",
    "    plt.title(\"acf of demo\")\n",
    "    plt.xticks(fontsize=15)\n",
    "    plt.yticks(fontsize=15)\n",
    "    plt.xlabel(xlabel=\"lag\",fontsize=17)\n",
    "    plt.ylabel(ylabel=\"ACF\",fontsize=17)\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(fname=fname)\n",
    "    print(\"saved...\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9003de8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.tsa.stattools import pacf\n",
    "\n",
    "def PACF(ts, lag=20, xlabel='', fname=\"\"):\n",
    "    lag_pacf = pacf(ts, nlags=lag)\n",
    "    plt.vlines(x=list(range(lag+1)),ymin=np.zeros(lag+1),ymax=lag_pacf,lw=2.0,color='black')\n",
    "    plt.axhline(y=0,ls=':',color='blue')\n",
    "    plt.axhline(y=-1.96/np.sqrt(len(ts)),ls='--',color='red')\n",
    "    plt.axhline(y=1.96/np.sqrt(len(ts)),ls='--',color='red')\n",
    "    plt.title(\"acf of demo\")\n",
    "    plt.xticks(fontsize=15)\n",
    "    plt.yticks(fontsize=15)\n",
    "    plt.xlabel(xlabel=\"lag\",fontsize=17)\n",
    "    plt.ylabel(ylabel=\"PACF\",fontsize=17)\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(fname=fname)\n",
    "    print(\"saved...\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "09e65e62",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "saved...\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,1, figsize=(12,4), dpi=150, facecolor=\"whitesmoke\")\n",
    "ACF(bjch[\"CCA\"], lag=30, fname=\"./fig/01.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9fc13cc",
   "metadata": {},
   "source": [
    "## BoxPierce LjungBox stats\n",
    "1. $Q_{BP}$suits large data samples，constructs one $\\chi^{2}$ stats\n",
    "2. $Q_{LB}$suits small data samples, used by default"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4670add2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lb_stat</th>\n",
       "      <th>lb_pvalue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>73.707719</td>\n",
       "      <td>7.080526e-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>82.700145</td>\n",
       "      <td>1.257522e-12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      lb_stat     lb_pvalue\n",
       "6   73.707719  7.080526e-14\n",
       "12  82.700145  1.257522e-12"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from statsmodels.stats.diagnostic import acorr_ljungbox\n",
    "\n",
    "acorr_ljungbox(bjch[\"CCA\"],lags=[6,12],return_df=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5d0e4fd2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lb_stat</th>\n",
       "      <th>lb_pvalue</th>\n",
       "      <th>bp_stat</th>\n",
       "      <th>bp_pvalue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>73.707719</td>\n",
       "      <td>7.080526e-14</td>\n",
       "      <td>64.094518</td>\n",
       "      <td>6.602230e-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>82.700145</td>\n",
       "      <td>1.257522e-12</td>\n",
       "      <td>70.470039</td>\n",
       "      <td>2.615619e-10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      lb_stat     lb_pvalue    bp_stat     bp_pvalue\n",
       "6   73.707719  7.080526e-14  64.094518  6.602230e-12\n",
       "12  82.700145  1.257522e-12  70.470039  2.615619e-10"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acorr_ljungbox(bjch[\"CCA\"],lags=[6,12],boxpierce=True, return_df=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa1a2d03",
   "metadata": {},
   "source": [
    "# modeling - sample:huozai\n",
    "1. model recognition\n",
    "2. parameter estimation\n",
    "3. model test & optimization\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6c2f95c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fire</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1860</th>\n",
       "      <td>1002.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1861</th>\n",
       "      <td>1000.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1862</th>\n",
       "      <td>998.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1863</th>\n",
       "      <td>998.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1864</th>\n",
       "      <td>998.92</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         fire\n",
       "year         \n",
       "1860  1002.75\n",
       "1861  1000.50\n",
       "1862   998.99\n",
       "1863   998.47\n",
       "1864   998.92"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "huozai_df = pd.read_csv('./data/huozhai.csv', usecols=['year','fire'], index_col=0)\n",
    "huozai_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e0370bec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6Sufcvuuu5jb3S083QxCkEp61tUqUl6u6pX9ERERE1D0Y5CMiIiIiIiIiCiErVkTDahUAAP36WTF1qrHN/WJjRYwcKa/Lx5KdREREROGFQT4iIiIiIiIiohBhNgPvv693bt9xRwuUyvb3P7dkJxERERGFDwb5iIiIiIiIiIhCxJdfRqGyUorqabUicnJaPO6fkSEH+QoLtRBFv3aPiIiIiLoRg3xERERERERERCFi6VI5i+/GGw1ITLR73N81k+/MGSWOH/eQ9kdEREREIYVBPiIiIiIiIiKiELBvnwrFxVrn9l13NXfYJjHRjqFD5XX5CgtZspOIiIgoXDDIR0REREREREQUApYujXY+HjPGjEsusXjYW+a+Lp/Ww55EREREFEoY5CMiIiIiIiIiCnK1tQI++UQu1TlvXsdZfA6u6/IVFTGTj4iIiChcMMhHRERERERERBTkVq/Ww2gUAACJiTZcf73B67bjx5ucj48eVeGnnzgcRERERBQOeFVHRERERERERBTEbDbgvffkUp2zZ7dAp/O+fb9+dgwYYHVu79jBbD4iIiKicMAgHxERERERERFRENu0SYtjx1QAAIVCxNy53pfqdHBdl6+wkOvyEREREYUDBvmIiIiIiIiIiILY0qVyFt/06Ub062fv9O/gunxERERE4YdBPiIiIiIiIiKiIHXkiBKbNsm1OefN63wWHwBMmCCvy1dWpkZNjXDefSMiIiKiwGKQj4iIiIiIiIgoSC1bJmfxDR1qwcSJZg97t2/wYBt697Y5t4uLWbKTiIiIKNQxyEdEREREREREFIRaWgSsXq13bs+b1wzBxwQ8QTh3XT6W7CQiIiIKdQzyEREREREREREFoY8/jkJDgzR0ExNjxy23GM7r92VkyCU7uS4fERERUehjkI+IiIiIiIiIKMiIIrB0qVyqc+bMFsTEiOf1O10z+fbuVaOpievyEREREYUyVaA7EAhvv/02tm/fjkWLFiEtLa3D/cvLy/HVV1+hvLwcBoMBycnJyM7OxtSpU6FQeI6T2mw2bNy4Edu2bcPp06ehVCqRkpKCGTNmICUlxaf+l5WV4S9/+QtEUcTSpUt9+h1EREREREREFLx27NDgwAG1c3vevJbz/p3Dh1sRH29HXZ0CdruAnTs1mDTJ1HFDIiIiIgpKERfkW7duHbZv3+71/uvXr8eKFSsgivJsuZMnT2LlypXYv38/HnroIahUbf8zms1m/PWvf0VZWZnbz/fu3Yt9+/bhnnvuwcSJEzvVf4PBgLffftutP0REREREREQUXv7v/+QsvqwsE1JSrOf9OxUKYPx4E9atiwIgrcvHIB8RERFR6Iqocp1btmxBXl6e1/vv2rXLGeDT6XS4+eab8fDDD2PGjBlQqVTYvXs3Vq9e3W77N9980xngGzhwIO6++248+OCDGD16NERRxLvvvovDhw936j289957qK6u7lQbIiIiIiIiIgodP/2kwFdf6Zzbd93V3GW/27VkJ9flIyIiIgptERPk+/LLL/Huu+96nQFnMpmwfPlyiKIIrVaLJ598EjNmzMCYMWNw880347777gMgZfqdOHGiVftdu3ahpKQEAJCamoo//OEPyMrKwmWXXYZHH30UY8aMgc1mw/Lly71+D4WFhSgoKPB6fyIiIiIiIiIKPStWRMNqldbL69fPiqlTjV32u12DfN9/r4HB0GW/moiIiIi6WdgH+YxGI9544w3k5+dDoVAgKyvLq3YbN25EXV0dAGDGjBkYNGiQ2/Pp6em46KKLIIoi1q9f36r9J598AgBQq9WYP38+1Gq5jr4gCJg5cyYEQcCRI0dQXl7eYX+qq6vx3nvvAQBiYmK8eg9EREREREREFFrMZmDFCr1z+447WqBUdt3vHzXKAr3e/vNrCfj+e2bzEREREYWqsA/yHTlyBMXFxYiOjsZjjz2Gyy+/3Kt2xcXFAKQg3aRJk9rcZ8qUKQDgzNhzOH36NE6ePAkAGDt2LBITE1u17du3L1JTUwFIWX+e2O12vPPOO2hpaUFiYiJuvPFGr94DEREREREREYWWr77SoaJCiupptSJyclq69Per1cBll8nZfIWFDPIRERERhaqwD/IBwKWXXornn38eI0eO9Gp/q9WK48ePAwCGDRsGvV7f5n5Dhw4FADQ2NqKiosL5c9d19i655JJ2XyclJaXV/m3573//iwMHDkChUOD+++9HVFSUV++DiIiIiIiIiELL//1ftPPxjTcakJho7/LXcF+XT9vlv5+IiIiIuocq0B3wt5SUFKSlpXWqTW1tLaxWKwBgyJAh7e6XlJQErVYLk8mEyspK9O7dGwBQWVnp9vrtGTBgQKv9z3Xs2DF8/PHHAKSyoUOHDsWZM2e8fzNEREREREREFBL27VOhuFgOut11V7NfXicjQw7y7dyphsUiZfgRERERUWgJ+yCf2oer1MbGRufj5OTkdvcTBAExMTEwmUyoqqpq1V6pVKJXr17ttu/RowcAoK6uDlarFSqV+5/DbDbjrbfegtVqxbBhw3DDDTd0+r24evrpp1v9TKPRIDc3FwA89pUkjr9RUlJSgHtCRNQ5PH4RUaji8YuIQllnj2GrV8uL76Wn2zF1arw/uoVp0wCNRoTZLMBgUODkyd4YP170y2sRUWjiNRgRhapIO35FRLnOzrJYLM7HjkBce6KjpTIatbW1rdpHR0dDoWj/n9jRVhRF1NXVtXp+9erVOH36NPR6Pe677z6Pv4uIiIiIiIiIQldNDZCXJzi3f/Obri/T6aDTwS2ot3Wr4GFvIiIiIgpWYZ/Jd746ygR0RIVNJpPPbdtqv2fPHmzYsAEAMG/ePCQmJnrVX09eeOEFj89XVVVBFDlzzxNH9N9TiVUiomDE4xcRhSoev4golHXmGPbmm9EwGOIAAImJNlx5ZQX8eegbO7YHtm6VJjZv2GDB3Lk1/nsxIgo5vAYjolAVSscvQRDQp0+f8/odTA1rg1Ipl8fQaDQe93UE6sxmuZ69o723bQH37MHGxkYsWbIEAJCdnY3x48d72XMiIiIiIiIiCjV2O/Dee9HO7dmzW6DT+fc1Xdfl27FDA7v/EgeJiIiIyE8Y5GuDzuVK2mq1etzX8bxrkM7R3tu2gHuQ8N1330V9fT2Sk5MxZ84c7ztORERERERERCFn0yYtjh2TJgIrFCLmzm3x+2uOG2eGUilV86mvV6C0lMWeiIiIiEINg3xtiImJcT6uqqryuG9zczMAuK2X52hfU1MDu4epcI62ru2/+eYbfPfdd1CpVLj//vuh1Wo7/waIiIiIiIiIKGQsXSpn8U2fbkS/fja/v2ZMjIiLL5YnLBcVea5GRERERETBh9O02hAbGwu1Wg2LxeIxyGe321FfXw/APfvPsX6ezWZDbW1tu+vp1dXVOR872hcUFACQsvz+53/+p8O+zps3z/maf/3rXzvcn4iIiIiIiIiCx5EjSmzcKI8pzJvX7GHvrjVhghnffy8F9woLtbjrLv9nEBIRERFR12EmXxsUCgUGDhwIADh27Fi7+506dQpGoxEA3AJ5gwYNcj721P7QoUPOxwkJCb52l4iIiIiIiIhC1LJlchbf0KEWTJxo9rB315owQX6toiINRLHbXpqIiIiIugAz+doxYsQI/Pjjj/jhhx9gNBrdMvUcysrKnI/79+/vfNynTx/07NkTtbW1KCkpwdixY9t8DUf7xMRE6PV6AEBOTo5bGc+27Nu3D1999RUA4IknngAAaDQsq0FEREREREQUSlpaBKxerXduz5vXDEHovtdPTzc5H1dWKnH4sBIXXeT/UqFERERE1DUY5GvH+PHj8fnnn8NoNGLDhg247rrr3J632+349ttvAUgBtiFDhrRqv3btWhQVFeGmm25Cr1693J4/evQojh49CgBIS0tz/tw1C7A9NTU1zscjR47szNsiIiIiIiIioiDxySdRaGiQiizFxNhxyy2Gbn39hAQRqakWlJaqAQBFRVpcdBFLdhIRERGFCpbrbMeAAQMwdOhQAMCaNWtQXl7u9vyaNWtw4sQJAEB6ejq0Wq3b85MnT4YgCLBYLHjrrbdgMsmz45qbm7FkyRLn9sSJE/31NoiIiIiIiIgoCIki8H//J5fqnDmzBTEx3V8v07VkZ2EhqwQRERERhRJm8nlw66234sUXX4TZbMZLL72E7OxsJCcnY/fu3di/fz8AKYtvxowZrdomJydjypQp2LBhA8rLy/HMM88gKysLoiji22+/dWbjjRo1yi2Tj4iIiIiIiIjC344dGhw4oHZuz5sXmAy6CRNMznUBi4oY5KPgU1mpwMqVehQWatDUpEBMjB2ZmWbk5LQgKcke6O4REREFFIN8HqSkpGD+/Pl45513YLVasXHjRrfnlUol5s+fj969e7fZPicnB/X19di5cycqKyvx8ccfuz2fnJyMe++912/9JyIiIiIiIqLg5JrFl5VlQkqKNSD9cM3kO3lShVOnlOjXj+vyUeAZDMDixXHIz9fDYnFfrHLzZh1efbUHZs1qwXPP1UOnC1AniYiIAkxobm7u/loQIebkyZP44IMPsGfPHoiiCEEQMHToUMyaNQspKSke24qiiK1bt+KLL77A2bNnAQBarRYTJ07Er371K0RHR3ts3x30emmR759++gmiyI+DJ0lJSQCAysrKAPeEiKhzePwiolDF4xcRhbL2jmFnzigwYcIFsFqlwMW779Zg+nRjt/fPYeLE3jh6VJoH/o9/1OKXv+zetQGJzmUwAHPnJqKgQNvhvpmZJixfXo2oqG7oWAThNRgRhapQOn4JgoA+ffoAAFpafKvqwEw+L/Tv3x+PPvooWlpaUF1djbi4OMTGxnrVVhAEZGVlISsrC5WVlTCbzUhKSoJGwxIYRERERERERJFoxYpoZ4CvXz8rpk4NXIAPADIyTM4gX2GhhkE+CrjFi+N+DvCJAAQPe4ooKNBi8eI45ObWd1PviIiIgoci0B0IJXq9HgMGDPA6wHeupKQk9OvXjwE+IiIiIiIioghlNgPvv693bt9xRwuUygB2CO4lO7kuHwVaRYUC+fmO74inAJ/8fH6+HpWVHOYkIqLIw7MfEREREREREVE3+eorHSoqpKieVisiJ8e30kxdKSNDDvIdOqRGVRWHiyhw8vJar8HXEYtFQF6evuMdiYiIwgyv2oiIiIiIiIiIusnSpdHOxzfeaEBioj2AvZEMGGBDnz425zaz+SiQCgt9+/wVFPBzS0REkYdBPiIiIiIiIiKibrBvnwo7dmid2/PmNQewNzJBkNblc2CQjwKpqcm34Upf2xEREYUynv2IiIiIiIiIiLrBsmVyFt+YMWZceqklgL1xN368XLKzsFDrYU8i/4qJ8S271dd2REREoYxBPiIiIiIiIiIiP6urE/Dxx1HO7WDJ4nNwXZfvhx9UqK/v3JpoRF3F9bPYGZmZvrUjIiIKZQzyERERERERERH52erVehiN0jBMYqIN119vCHCP3A0dakVCgrQunygKKC5myU4KjJycFqjVYqfaqNUicnJa/NQjIiKi4MUgHxERERERERGRH9nt7qU6Z89ugU4XwA61QRCACRPkTCiuy0eB0ru3HbNmOQJ2HQX7pOdnzWpBUhLLdRIRUeRhkI+IiIiIiIiIyI82bdLi2DEVAEChEDF3bnBmHLkG+bguHwXSs8/Wo29fK4COysYKyMw04bnn6rujW0REREGHQT4iIiIiIiIiIj9aulTO4ps+3Yh+/WwB7E37XNdC27NHjZYWrstHgVFVpURVlbLD/aKi7Pi//6sOusxYIiKi7sIgHxERERERERGRnxw6BGzcKEcg5s1rDmBvPBsxwoKYGKnkodUqoKREHeAeUaR68cUeMJulIHOvXjY89lgDsrONGDvWjIwME5RKqUynwaDAN98wwkdERJFLFegOEBERERERERGFk8pKBVau1KOkRIl9++RsuMGDLZg40eyhZWAplcD48WZnULKoSIsrrgje/lJ42rlTjTVr9M7tp59uwK23Gtz2eeiheHz8sbTPsmXRuOEGY7f2kYiIKFgwk4+IiIiIiIiIqAsYDMDChXFIT78AubmxWL9egTNn5CDf8eMqLFoUB2MQxyPc1+XTBLAnFIlEEXj22Tjn9qhRZsycaWi13513yhmxBQValJYyj4GIiCITg3xEREREREREROfJYADmzk3EihXRsFjaXsvOZhOwYkU0br89EYbWcYugMGGCyfm4pEQDMxP5qBt99pkOJSVycPmPf2yAoo3Ry3HjLBg1Sv5wLlsW3XonIiKiCMAgHxERERERERHReVq8OA4FBVoAYgd7iigo0GLx4rgO9guMSy6xQKeT3oPRKGD3bq7LR93DaAReeCHWuT19uqHd8raCAMyb1+Lc/uijKDQ2th1cJyIiCmcM8hERERERERERnYeKCgXy8x1riHUUaJCez8/Xo7Iy+IZlNBpg7Fg5sFJUpA1gbyiSLFkSg5MnpbKbKpWIZ55p8Lj/TTcZEB9vBwA0Nyvw4YdRfu8jERFRsAm+q0kiIiIiIiIiohCSl6dvt0RneywWAXl5+o53DADXdfmKirguH/lfVZUCr78e49yeN68ZQ4bYPLaJihIxa5aczbdsWTTEjhJpiYiIwgyDfERERERERERE56Gw0LdAWEFBcAbQXNfl27FDA5vnWAvReXvllR5oapKGKePj7XjkkUav2t1xR7PzcXm5Gtu2Bed3ioiIyF8Y5CMiIiIiIiIiOg+O4ER3tfO3ceMsUKmklKimJgV++IHr8pH/lJWpsGKFnNX66KON6NnTu5S8wYNtmDzZ6Nxetiy6y/tHREQUzILzapKIiIiIiIiIKETExNi7tZ2/6fUiRo+2OLd9zVQk8saf/hQLu10qdzt4sNUtO88bd94p7792rQ6nT3O4k4iIIgfPekRERERERERE5yEjw9zxTm3IzPStXXfIyJBLdnJdPvKXTZu02LRJ59z+wx8aoOnkx23KFBMGDLACAGw2Ae+/z2w+IiKKHAzyERERERERERGdh5ycFqjV3pUXdFCrReTktPipR+dvwgQ5AFlYqIHYubdH1CGrFXjuuVjndmamCVdfbfTQom1KJXDHHfJ3aeVKPczBGz8nIiLqUgzyERERERERERGdh9697Zg1yxFk6CgaJj0/a1YLkpKCs1wnAKSnmyEIUl9ra5UoL1cFuEcUblau1OPgQWm9R0EQsXhxAwTBt991220t0Gqlz2tlpRJffhnVVd0kIiIKagzyERERERERERGdp2efrUdmpglAR1EKAZmZJjz3XH13dMtncXEiRoywOrdZspO6UkODgFde6eHcnjnTgIsvtnho4VlCgh033GBwbi9bpj+v/hEREYUKBvmIiIiIiIiIiM5TVBSwfHk1pkxpv9ygWi1izpxmvP9+NXS6dncLGlyXj/zln/+MQXW1EgAQFWXHwoUN5/07581rdj7esUOLH35g9ikREYU/BvmIiIiIiIiIiLpAVBQwcaIcGEtIEDFhgh3Z2UYsWtSA4uKzyM2tD4kAH+C+Ll9BgZbr8lGXOH5ciX//O8a5/dvfNqFPn/MvXTtmjAWXXCJ/ZpcujT7v30lERBTsOKWFiIiIiIiIiKiLlJaqnY/vuMOO3Fw7KitrAtgj37kG+c6cUeLECSUGDrQFsEcUDl58MRZms1TWNjnZhvvvb+6ghffuvLMZjz0mZZ1+/HEUfv/7BsTFMTpN5KqyUoGVK/UoLNSgqUmBmBg7MjPNyMkJ7rViiahtzOQjIiIiIiIiIuoipaXyfOpRo0I7uNCrlx0pKfI6aYWFLNlJ52fnTjU++yzKub1oUQP0+q77ntx4owHx8VKQwmBQ4IMPuDYfkYPBACxcGIf09AuQmxuLzZt1KCnRYPNmHV56KRbp6Rdg4cI4GNuvOk1EQYhBPiIiIiIiIiKiLmC1AgcPypl8oR7kA9yz+bguH50PUQT+53/inNsXX2zGLbcYuvQ1oqKAnJwW5/ayZdGwMzGJCAYDMHduIlasiIbFIrS5j8UiYMWKaNx+eyIMXfvVJCI/YpCPiIiIiIiIiKgLHD2qgskkDZ4qFCLS0gLcoS7gGuQrLNQGsCcU6j77TIfvvpMDxYsXN0Dhh5HJO+5ohiBIAfbDh1XYupWfW6LFi+NQUKAF0NHkExEFBVosXhzXwX5EFCwY5CMiIiIiIiIi6gIHDsilOgcPtiIqysPOISIjQw7yHT2qwpkzHEqizjMYgOefj3Vu/+IXBmRmmj208N3AgTZMmWJybi9dypKdFNkqKhTIz3d8D9rO4pNJz+fn61FZyeM9UShQdbwLBdLZs2fx3//+FwcOHEBNTQ2USiX69euHzMxMTJ48GQp/THkiIiIiIiIiok4rLZVLdaamWhEOwy79+tnQv78VJ09K76WoSIMZM7hgE3XOkiUxOHVK+gyp1SKefrrBr683b14zNmzQAQC+/lqHU6eU6NfP5tfXJApWeXn6dkt0tsdiEZCXp8eCBU1+6hURdZXQv9r0wdtvv43t27dj0aJFSPOidkZ5eTm++uorlJeXw2AwIDk5GdnZ2Zg6dWqHQTabzYaNGzdi27ZtOH36NJRKJVJSUjBjxgykpKR4bPv999/jX//6F8xm95lNhw4dwqFDh7Bjxw4sWLAA0dHRHb9pIiIiIiIiIvIr10y+tDQLwmXYZcIEs0uQT8sgH3VKZaUC//hHjHN73rxmDBni34DbpEkmDBpkxdGjKtjtAt57T4+nnmr062sSBavCQt/WUy0o0GDBgi7uDBF1uYhLA1u3bh22b9/u9f7r16/HCy+8gJKSEjQ2NsJqteLkyZNYuXIlXnvtNVit1nbbms1m5ObmYsWKFTh69CjMZjMMBgP27t2L559/Htu2bWu3bVVVFd58802YzWYkJSXhzjvvxDPPPIMHHngAw4cPBwCUlZVh9erV3r95IiIiIiIiIvIb10y+tLT2xwtCjWvJzqIi3waLKXK98koPNDVJQ5Dx8XY88oj/g20KBTB3brNzOy9PD5PJQwOiMOb4/nVXOyLqXhH1Td2yZQvy8vK83n/Xrl1YsWIFRFGETqfDzTffjIcffhgzZsyASqXC7t27PQbZ3nzzTZSVlQEABg4ciLvvvhsPPvggRo8eDVEU8e677+Lw4cNttl26dCmMRiOSk5OxePFiTJ48GSkpKUhPT8eTTz6JsWPHOt9TdXV1J/4ViIiIiIiIiKirNTcLOHZMztxLTbUEsDdda8IEOTpSWqpGTU3nyr5R5CotVWHlSnlNvMcea0R8vNgtr33rrS3Q6aTXqq5W4osvwmCRTCIfxMTYu7UdEXWviAnyffnll3j33Xchit5dSJhMJixfvhyiKEKr1eLJJ5/EjBkzMGbMGNx888247777AEiZfidOnGjVfteuXSgpKQEApKam4g9/+AOysrJw2WWX4dFHH8WYMWNgs9mwfPnyVm1PnDiBffv2AQDuvPNOxMTEuD0vCAKmT58OABBFET/++KP3/xBERERERERE1OXKyuQAn15vx8CB4bP+15AhNiQlye9n505m85F3/vSnWNjtUlB4yBAr7rijuYMWXadnTxE33dTi3F66lMvdUGRyzcbujMxM39oRUfcK+yCf0WjEG2+8gfz8fCgUCmRlZXnVbuPGjairqwMAzJgxA4MGDXJ7Pj09HRdddBFEUcT69etbtf/kk08AAGq1GvPnz4daLZfsEAQBM2fOhCAIOHLkCMrLy93anjp1CgMHDsSwYcPaXTMwLi7O+fjcNfuIiIiIiIiIqHsdOCDf9w8fboUijEZcBEFal8+hsFAbwN5QqNi0SYtvvtE5t//wh3q4DI91i3nz5CBfSYkGe/d2cweIgkBOTgvU6s5l0KrVInJyWjrekYgCLowuOdt25MgRFBcXIzo6Go899hguv/xyr9oVFxcDkIJ0kyZNanOfKVOmAIAzY8/h9OnTOHnyJABg7NixSExMbNW2b9++SE1NBSBl/bnKyMjAc889h6effrrd/lVUVDgft/X7iYiIiIiIiKj7lJbKmXxpaeFTqtMhI0Mu2cl1+agjVivw3HOxzu3LLzdh2rTuXxTv4ostGDtWDlAvXar3sDdReOrd245ZsxwBu46CfdLzs2a1ICmJ5TqJQkHYB/kA4NJLL8Xzzz+PkSNHerW/1WrF8ePHAQDDhg2DXt/2BcDQoUMBAI2NjW5BN9d19i655JJ2XyclJaXV/t7asGEDACA6OtrZDyIiIiIiIiIKDNdMvtRUawB74h+umXx796rR1MR1+ah9K1fqcfCg9J0QBBGLF9dDCNBHZt48uUTop5/qUVvLzy5FnmefrUdmpglAR59/ATqdiKefbuiObhFRFwj7IF9KSgoeeeQRxMfHe92mtrYWVqt0QT5kyJB290tKSoJWK5WoqKysdP7c9bEjkNeWAQMGtNq/I2azGXl5edi9ezcA4JZbboFKpeqgFRERERERERH5iyi6Z/KlpoZfJl9qqhVxcVJWh80mYNcuZvNR2xoaBLzySg/n9qxZBowaFbjA9/XXG5CYKK0paTQKWL2a2XwUeaKigOXLq9Grl6f1YqUsPqNRwKuv9vCwHxEFk7CPDql9KPbd2NjofJycnNzufoIgICYmBiaTCVVVVa3aK5VK9OrVq932PXpIB8u6ujpYrVaPwbqtW7fi4MGD+P7779HQIM2kuOGGGzB58mTv3hTQZvlPjUaD3NxcAPDYV5I4/kZJSUkB7gkRUefw+EVEoYrHLyIKBadPA7W1Suf2FVfEoVev8DuGXXEF8J//SI/37InHLbewlBu19re/KVBdLX0f9HoRL72kDvh34O67gZ+Hv7ByZSx+/3t9WK2b6Q/hdvwi4IcfgKoq+Vw1frwdggD06AFkZ4s4dQp46y3p+SVLYnDrrTpMmtS5tfyIgkGkHb/CPsjnC4tFnnHnCMS1Jzo6GtXV1aitrW3VPjo6GgoPVwzR0dEAAFEUUVdX5zHI9sknn6C6utq5nZqaihtuuMHzGyEiIiIiIiIiv9u3Ty5/1qePiHCdQ3vFFaIzyLd1K0seUmtHjgCvvy6PhT3+uB19+wawQz+79147XnlFAbtdwI8/Cli3TsA11zB4QZFl6VL5u3nppSK2bnXP6jMagc2bFThwQDq+z5+vREmJFR0MjxNRgDHI14GOMgEdUWGTqfXiwd62ba+9q9mzZ6O0tBQlJSWorq5GaWkpfv/732PhwoVeR6RfeOEFj89XVVVBFHmB44nj37ozJVaJiIIBj19EFKp4/CKiUFBUFA0gDgAwbJgJlZU1AMLvGDZqlBqA9J527BBw4kQldLrA9omCy+OP94TZLI2HJSfbMHduBSorAz/WFBUFTJvWE2vXRgEAXnvNinHjagLcq+AWbsevSGcyAe+9d4Fze+bMelRWtrTa79VX1bj++l6w2QQcOybgoYfMePnl+u7sKtF5C6XjlyAI6NOnz3n9Diamt0GplNOWNRrPNeYdgTqzWV6A2tHe27aAe/ZgW8aNG4c5c+bg5Zdfxp133gmlUonKykr87W9/c3ttIiIiIiIiIupeBw7Ik3xTUwO39pi/XXyxBVFRUolOs1nA999H7rp8lZUKvPZaDHJyEnDDDb2Qk5OA11+PQWVl5A61FRer8fnnUc7tp55qgF4f+ACfw7x5ckBj40Ytjh9XetibKLysW6dDTY30mdfpRNx8s6HN/UaPtuDhh5uc2ytXRmPDBm239JGIfBO5Vx4e6FymoVmtni/OHc+7Bukc7b1tC8DrQJ1CocDkyZNx2223AQBOnz6NgoICr9oSERERERERUddzD/J5nsQbytRq4LLL5PdXWBh5QT6DAVi4MA7p6RcgNzcWmzfrUFKiwebNOrz0UizS0y/AwoVxMBr914dgDDDa7cCzz8Y5t0ePNuOXv2w7iBAoV1xhwuDB0licKAp4773oAPeIqPusXKl3Pr7uOgPi4toPwC9Y0IiLL5bHqp94Ih61tSzRTBSsGORrQ0xMjPNxVVWVx32bm5sBwG3tPUf7mpoa2O3tL0LtaHtue29kZ2c7MwbLy8s71ZaIiIiIiIiIuobVChw6JFfqGTEifIN8ADBhgrzcSFFRZAX5DAZg7txErFgRDYul7QFvi0XAihXRuP32RBi6OMYVDAHG9nz2WRS++07+PCxe3IBODnX5nUIB3HmnPBaXl6fv8r8RUTA6flyJzZvlpJbZs1uX6XSlVgOvvVYHjUYKBJ49q8Qzz8R5bENEgRNkp9vgEBsb61xPz1OQz263o75eqknsmv2XmJgIALDZbKitrW23fV1dnfOxro0i9p7Wx9NqtYiLkw6uLS2eD8xERERERERE5B9HjqhgMkkBH6VSREpK+JbrBICMDDm7Y+dODTpYfSSsLF4ch4ICLYCOSlCKKCjQYvHirhsUD3SAsaO+vfBCD+f2tdca3D4nwWTWrBZnydm6OgU++yyqgxZEoW/1ajmLb/BgKyZM6Pj7OXy4FQsXNji3P/1Uj88/5yKsRMGIQb42KBQKDBw4EABw7Nixdvc7deoUjD9Pj3IE9gBg0KBBzsee2h86dMj5OCEhwfl46dKlWLBgAVavXt1uW1EUYfj5ii06muUFiIiIiIiIiALhwAE5i2/wYCvamMMbVi691OzM7mhpUWDfPnUHLcJDRYUC+fmOgfKOytZJz+fn67ushGYgA4wdeeedGJw6JX0P1GoRTz/d0EGLwImLE93KiC5bxjE1Cm82G7BqlRzkmz27BYKXlTfvvbcZ6ely9vZTT8VF9LqjRMGK38p2jBgxAgDwww8/OAN55yorK3M+7t+/v/Nxnz590LNnTwBASUlJu6/haJ+YmAi9Xj7YKpVKNDQ04Ntvv3XL9nNVWlrqDPK5BhXJPxz17q+9VomsLGVQ1LsnIiIiIiKiwHNfjy+8s/gAICpKCvQ5RErJzrw8fbsZdO2xWAS3dbB8FegAoyeVlQr84x/ysjd33dWMwYNtfn/d8+FasnP3bg2++y4yAtUUmb75RoszZ6Qln1QqETNnel8RTqkE/va3Omf2a22tEgsXxsFD8TkiCgBVx7tEpvHjx+Pzzz+H0WjEhg0bcN1117k9b7fb8e233wIANBoNhgwZ0qr92rVrUVRUhJtuugm9evVye/7o0aM4evQoACAtLc3tuezsbGzYsAEGgwFvvPEGHn/8cWi1WufzJpMJeXl5ztceP358l7xnas1gkGbL5eefezGvw+bNOrz6ag/MmtWC556rD/vZmkRERERERNRaaak8tJKWFhm1KydMMGPHDmmcoqhIg/vvb+6gRegrLPQtmJmbG4vXXusBnU50/hcV1fZj9204t7dt0/gUYMzL02PBgiaf+u2tl1/ugeZmKZgYH2/Hww83+vX1usLIkVakp5tQXCx9hpcti8aYMXWB7RSRn+TlyRMNrr7aiKQke6faDx5swzPPNOD3v48HAKxbF4UPPjBi1iwuaEkULBjka8eAAQMwdOhQlJeXY82aNRg2bBiGDh3qfH7NmjU4ceIEACA9Pd0tCAcAkydPxrp162CxWPDWW2+5Beqam5uxZMkS574TJ050a3vhhRciOzsbmzdvRnl5Of74xz/iqquuQnJyMmpqavD111/j1KlTAIBZs2ahR48eoK7nqHcvlcNom6Pe/eHDKixfXo0olnInIiIiIiKKKKWlchZQWlr4Z/IB0rp8//iH9HjHDi3sdkAR5oVumpp8f4MmkwCTSUB9fRd2yAsFBRosWNB1v6+yUoGVK/UoLNSgqUkBQQBKSuTP/+9+14j4+NBI8Zk3r8UZ5Pvssyj88Y8NSEjoXPCDKNhVVCjw9ddyVkJOjvdZfK7uvLMFa9dKCQ8A8Mc/xmHiRBP69eN3higYMMjnwa233ooXX3wRZrMZL730ErKzs5GcnIzdu3dj//79AKRMuhkzZrRqm5ycjClTpmDDhg0oLy/HM888g6ysLIiiiG+//RY1NTUAgFGjRrXK5AOAO+64AwaDAcXFxTh79ixWrlzp9rxSqcQtt9yCqVOn+uGdE3BuvXtPM+bkeve5ud18xU5EREREREQB09Qk4PhxeWglNTUyMvkuu8wMhUKE3S6grk6BsjJV2Ac4Y2JCbzB71y4Nnn46DiNGWJCWZkFqqhXR0Z0PwrVf5UjWo4cdM2eGTkbntdcakJQUi8pKJUwmAatW6fHb3/o365Gou334oR5Wq/Sd7dPHhiuvNHXQom2CALzySh2uuqo3GhsVaGxU4He/64m8vGqv1/cjIv9hkM+DlJQUzJ8/H++88w6sVis2btzo9rxSqcT8+fPRu3fvNtvn5OSgvr4eO3fuRGVlJT7++GO355OTk3Hvvfe22ValUuGBBx7Arl27sGnTJhw5cgRGoxHx8fEYMWIErrnmGvTr169r3ii14mu9+yeeaOx02jsREXWtc2cYx8TYkZlpRk5OC4/RRERE1KVcS3Xq9XYMGBDca5F1lZgYERdfbMHu3VIJy6IiTdgH+TIyzM4sls74zW8acdttBhgMAoxGAQYDfv6/tH3u47ae27VLg7q6zmcSNjcrsGxZtHNbEERceKENI0ZYfg78WZGWZsGAAbZ2MzG9qXIEAI2NCtx1V2LIVDnSaIDZs1vw2mtSdaz33tPjvvuaoFQGuGNEXUQU4bYm6G23tZzX57tfPzuee64ejz7aEwCwZYsWy5bpMW+eb9mBRNR1hObm5tDIow+gkydP4oMPPsCePXsgiiIEQcDQoUMxa9YspKSkeGwriiK2bt2KL774AmfPngUAaLVaTJw4Eb/61a8QHR3tsX130OulA/5PP/0EkSunAgBeey0GubmxnW63aFGD3+vdExH5IikpCQBQWVkZ4J74T0czjNVqkeuoEoWgSDh+EVHoev99PRYtigcAjB1rxuefV7k9H87HsP/5n1j8+98xAIAbbjDgzTdrA9wj/6qoUGD8+As6tTaeWi2iuPjseU8083WMwlsxMXakplqdGX8jRkhZfzExIhYujMOKFdHwpsoRIGDOnOaQqXJ0+rQCGRkXwGaT3tfSpdWYNs23TKdwFM7Hr0hQUKDBLbf0AiAF+AsKKs57IoooAr/+dU+sWydF8qOi7Pj660oMHhwZE1wodITS8UsQBPTp0wcA0NLiW9CcmXxe6N+/Px599FG0tLSguroacXFxiI317uJKEARkZWUhKysLlZWVMJvNSEpKgkbj24LN1D18XVC7q+vdExGRd7iOKhEREQWCayZfWlpklOp0yMgw49//lh4XFWkgigjrsm29e9sxa1ZLpwJes2Z1TSWJnJwW/O1vPToVYFQqRSxc2IDjx1U4cECN0lIVWlraTtdralJg504Ndu50Hwvp39+KU6ccqT/hV+Wob187pk834ssvpRuDZcuiGeSjsOGaxZedbeqSTHNBAHJz61FcrEFtrRIGgwKPPhqPjz6qZhYsUQCF+bLIXUuv12PAgAFeB/jOlZSUhH79+jHAFwJ8XVD7fBbiJiIi37mvo+qJvI5qV6usVOC112KQk5OAG27ohZycBLz+egwqK3luICIiClelpWrn49TU8C5Xea7x483OxxUVShw5Ev4jvM8+W4/MTBO8CXhlZprw3HNdk9HmCDBKOr7eBaTSfA8+KGXVff55FcrKzmDr1rP4979r8OijjbjmGgMuvNDzZ/bkSRVEsXORW4tFQF6evuMdg8Sdd8rrCG7apIuIzzGFv7o6wRm8BqSJAl0lKcmOv/xFPrYVF2vx9tuBr1RHFMk46kTUBl8X1A7FhbiJiEKdr+uodlXwzWAAFi6MQ3r6BcjNjcXmzTqUlGiwebMOL70Ui/T0C7BwYRyMxi55OSIiIgoSoggcOOAa5IusTL6EBDuGD5ffc1GR5zXbwkFUFLB8eTWuuKL9Czu1WsScOc14//3qLi0Rf74BRoUCGDzYhmuvNeLxxxuxZEkttm+vQGnpT/j000q88EId5s5txrhxZkRHn9/YRkFB6ExunzjRjKFD5c/xe+8xWEGh79NPo2A0SseKhAQbpk/v2pvR66834qab5MBhbm4syspYMJAoUBjkI2pDRoa5453akJnpWzsiIvJdXl7ba/B50lUzjB1lQlesiG63D44yobffngiD4bxfkoiIiILEmTMK1NXJwyqRFuQDgAkT5HtgX5e9CDVRUVJmncMFF9gwdqwZ2dlGLFrUgOLis8jN7fo1oB0BxjlzmqFWt53N50uAsUcPEenpFtx5Zwv+8pd6fPZZFUpLz2D79rMYMsS37NRQqnIkCO7ZfKtX62EwhHHdWQp7ooifywpLZs40wB9F5f7853pccIFUAtRsFvDww/GwRN5pkCgoMMRO1AZf6t2r1WKXpr8TEZF3fB1Q+t//jcGePWr06mVHUpIdvXrZ0Lu39P+kJOln0dGeyyG5lwn1vC6Lo0xobm7XlG0iIiKiwHIt1ZmcbENCQkdlFMNPRobJmflUVBQZQT6LBdiwQY6g/fnP9bj22u4p2RAVJa2H9cQTjcjL06OgQIOmJgViYuzIzDQjJ6dr1gBUKIALL7Shf38rDh/u/NBhqFU5uuUWA158MRbNzQrU1yvw6adRHN+hkLV3rxo//CCfn/z1We7ZU8TLL9fhjjsSf35dDf7xjxg89liTX16PiNrHIB9RGwK5oDYREXWOrzOFGxoU+OqrKI/76PWOAKAdSUk29OpldwYCNRoRq1Z1vkzoE0808nxBREQUBkpL5SGVSMziA9zX5TtxQoVTpxTo1y+8r3OKizWor5euPzUaEVdeaer2PiQl2bFgQRMWLPDv62RkmLF5c+dTEkOtylGPHiJ+9SuDM2C9dKket93WAoEJfRSCVq6UK9akp5swdKj/1ou96ioTZs9uxsqV0nfntdd6YOpUE0aPjsxzIlGghE7+PFE3C9SC2kRE1Dn+nCnc0qLAsWMq7NqlwX//G4X334/Gq6/2wNNPx+Pxx3vCZgtMmVAiIiIKPNdMidRU/w2iBrM+fewYNEh+7zt2hP+6fOvWyUGvK64wdVj5IZTl5LS0Wxq0PaFa5WjePLlk5759GuzapfawN1FwamkR8Mkn8kTW7vguLl7cgAEDpPOA1SqV7eR69ETdi0E+onZ4U+9epfLPgtpEROQ9X9dRvfZaA37/+wbce28TfvnLFlxxhQmpqRYkJtogCP4brCkoiIxSVkREROHOtVxnWlrkZi1E0rp8ogh8/bV88z9tWniPZDuqHEk6uj6Wng/VKkfDh1t/nugtWbYs2sPeRMHp8891zko3MTF23HCD/49RMTEiXn21zrl98KAar7wS6/fXJSIZy3USeXBuvfudO2OwebPgXKvvxRfrMHu2IcC9JCKKbL6uo/rCC/XtDkBYrUBNjQIVFQpUVSlRWalAVZUCFRVKVFUpUFmpxM6dahgMnZ8v5Wt5USIiIgoeFgtw6BDLdQLAhAkmrF4tVSoI93X5ystVOHpU/rtPnRreQT5AqnJ0+LDq53WoPQn9Kkd33tnsfJ9ffBGFxYsb0KtX6AUsKXK5Vo256SYD9PruyTS+/HIz7r67CUuWxAAA3nwzGldfbXQr6UxE/sNRJiIvOOrdf/WVDb/8pXyCdC3PQkREgeGPGcYqlfR7R42yYtIkE2bONOA3v2nG4sUN+Mc/6rBqVTXS0327YfFneVEiIiLqHkeOqGA2SxOMlErRr2seBTvXqgrl5WpUVYXvUJNrqc7Ro83o2zf8r+u8qXKkVodHlaNrrjEiOdkGADCbBbe1zYiCXXm5CsXFcjB+9uzuLZv71FMNuOgiacKLKAp45JF4NDdzYUui7hC+V15EfpKdLV/UFhaG/3oDREShIBDrqPpaJjQzk7MZiYiIQt2BA3I215AhVmgj+NZw4EAbkpJszu2bb+6FnJwEvP56DCorw2vYyTXId/XV4Z/F5+CoclRcfBaLFjUgO9uIsWPNyM42YtGiBhQXn0Vubn1IB/gAQK0G5syR1+ZbvlwPa+TG7ynEuGbxjRxpwejR3ZthHhUF/P3vdVAopHHTY8dUeP55lu0k6g7hdbVF1A2ysuSZeqWlKtTWclYKEVGgOWYYDx7c/o1MV88wzslpaXc2s6c+dMfi50RERORfBw64rscXuVEAgwFYtCjOLXvv8GEVNm/W4aWXYpGefgEWLoyDMQziYZWVCpSUyH/3cF+Pry2OKkd5eTX4/PMq5OXVYMGCppBcg689s2e3QKWSrvFPn1Zh/foQj1xSRDCbgQ8+iHJuz57dDCEAw5Vjx1rwwANNzu1ly6KxeXMEz4Ih6iYM8hF10vDhQK9e0ixFURSwYwdPVkREwUCtBmprlc7ttDSLX2cY+6NMKBEREYWG0lI52BOp6/EZDMDcuYlYsSIaotj2aLLFImDFimjcfnsiDCG+nP2GDVrn++zb14qRIyM3uBvOkpPtuOYaOYC7dGl0AHtD5J1163SoqZHuhXU6ETfdFLgD7qOPNiItTT4vPvZYPOrrmSBB5E8M8hF1kiAAEybIpdYKCsJ7YXEiolCxY4cGdXXSpY1GI+LTT6v8PsM4EGVCiYiIKPBKS+Vyna6DmZFk8eI4FBRo4c1kp4ICLRYvjuuObvmNe6lOU0CyZKh7zJsnl+zcskWLQ4eUHvYmCjzXUp3XXmtAfHznKs50Ja0WeO21WmfVm59+Uob88Z8o2DHIR+QDaUBXUlTEIB8RUTBYu1YeeJk40YSYGP/f2DjKhM6Z09xu6U6lsmvLhBIREVFgNTYKOHFCDvKlpkZeRldFhQL5+Y5B5Y4nOwFAfr4+ZNfoMxiAb7+Vq/hE0np8kSgjw4zhw+Xg/XvvMZuPgteJE0q349Ps2YFfHmLkSCsee6zRuf3BB3q3+3Ui6lqheXVFFGAZGXIm3759ajQ0cAofEVEgieK5s6u7b+AlKgrIza1HcfFZLFrUgOxsI+Li5KzBe+9t6tIyoURERBRYrll8MTF29O9vC2BvAiMvTw+LpXP3wRaL4JZtEkq2btXCaJSG0GJi7MjIMHXQgkKZIAB33iln8y1frsesWYm44YZeyMlJwOuvx4RswJrCz+rVemcp4UGDrG5jloH02982YcwYuS8LF8ahuprfGyJ/4DeLyAfDh1sRHy8N4NrtAoqLmc1HRBRIpaUqHD8uD7gFYnZ1UpIdCxY0IS+vBvfcIy82/uOPKg+tiIiIKNS4rsc3fLgViggcWSks9O0eOFSXu/j6a3m21qRJJmi1HnamsHDddQaoVFKlDrNZgW3btCgp0WDzZh1eeikW6ekXYOHCOBiZ1EkBZLMBq1bJkydmz24JmlLCKhXw97/XQqeTvkdVVUo8+WQcxMBVEiUKWxF4KUp0/hQKYMIEeeaerzc4RETUNVxLf1x6qRnJyV2//l5nXHyxXN5nzx6eI4iIiMKJa5AvNTUy1+NravJtOMnXdoFkt7sH+ViqM/wZDMD99yfAam0/WmKxCFixIhq3354Ig6EbO0fk4ttvtfjpJ2nNSJVKxMyZgS/V6SolxYannmpwbn/5ZRSWL9fjtddikJOTwOxYoi7Cbw+Rj1zT3wsLOY2PiCiQAlWqsz2jR8sDfmfOKHnDQkREFEYOHJCz9NPSIjPIFxPj24QqX9sF0u7dalRUSIPoSqWIKVMCf61J/rV4cRwKCrQAOko5ElFQoMXixXHd0S2iVlxLIE+bZkTv3sF3jP31r5uRmSknSjz1VBxyc2OxebOO2bFEXYQjTkQ+cg3y7dmjRktLkOTDExFFmJ9+UmD3bjlbbvr0wN8VXHCBHRdcIK/Ps2eP2sPeREREFCpE0T2TLy3NGsDeBI6vaz5lZgbHWlGd4TqZbPx4M3r2ZK25cFZRoUB+viNw0tE4j/R8fr6ek/qo21VWKtyOTzk5wZXF56BQAC++WAeFwnHsbPt7xexYIt/xDETko5EjLejRQ5ohY7UK2LmT5diIiALBtXzSwIFWDB8eHINto0bJM/v37mWQj4iIKBz89JMC9fXyUEqkluvMyWmBWt25YJdaLQbtILQnrtea06YFfjIZ+Vdenh4WS+cmcVssgltGFVF3+PDDKGdJ2eRkGyZNMnXQInD+/e8Y2O3efK+YHUvkCwb5iHykVALp6fIsxFBdQJyIKNSdW6ozWBYady3ZySAfERFReHDN4ktOtiE+PjKzunr3tmPWLEfAruOShgAwa1YLkpKCr5ScJ8ePK3HggPw3Z5Av/BUW+ja2wzEh6k6iCKxcGe3cvu22FiiVAeyQB+7ZsR1hdiyRL1Qd73J+6uvrUVhYCK1Wi0mTJrk9t3btWmzatAnV1dVISEhAVlYWrr32WigU/BJTaMjMNGPjRmlwuaiIF3RERN2tqUnAtm3yuqjBUKrTYfRo97LOREREFPpcAz6Ruh6fw7PP1uPwYdXPa5d5IiAz04Tnnqvvln51JdcsvqFDLRgyxOZhbwoHTU2+jUn62o7IF0VFGhw+LA3rC4KI224L3izp88mOXbCgyU+9Igovfj0D7d+/H0899RRWrVqFvXv3uj23atUqrFq1CmfPnoXVakVFRQU++ugjLFmyxJ9dIupSEybIqfDffadhzWgiom62aZMWZrN0wxAfb8f48cGzzsvFF8sDf6dPq1BdzRt/IiKiUFdaKs+VjvQgX1QUsHx5NebMafZYuvOyy0x4//1q6HTt7hK0zq0YQeEvJsa3bFNf2xH5YuVKOTMuK8uEAQOCdwICs2OJ/M9vo011dXX45z//CUMbUY+KigqsXbvWua1zudLbvn07SktL/dUtoi41erQFer10IWc2C/juO56AiIi6k+vAy1VXGaHye40C7yUn25GUJN9ssWQnERFR6HPN5EtNDY51gAMpKgrIza1HcfFZLFrUgOxsI8aONWPAAPnfprlZAW1HyX5BqL5ecBucZqnOyJCR4dukwczM4JlsSOGtvl7Af/4T5dwO9rVOmR1L5H9++7asX78eRqN0ATRp0iTceuutzue2bdsGAOjZsyeef/55/O///i9mzpzpfH7z5s3+6hZRl1Krgcsuky/kfJ2dQkREnWexABs2yEG+YCrVCQCC4J7Nx5KdREREoc1iAQ4dkmcUpaZGdiafq6QkOxYsaEJeXg0+/7wKK1ZUO587cEAdktdB33yjhdUqVYxITLRh7Fj+vSNBTk6Lx8zUtqjVYtAHWih8fPJJFIxG6diUkGALuvvgczE7lsj//Bbkc5TnTEhIwJ133onevXs7n9u/fz8A4MYbb0Tfvn0BANdeey0uuOACAMCPP/7or24RdTnXWV6FhSE4PZGIKEQVFWlQXy9dymi1IiZNMnXQovu5BvmYyUdERBTafvxR5VxXSKkUkZLCTL72XHSRDePHy9dmeXl6D3sHJ9eKEVOnmqBUBrAz1G1697Zj1ixHwK6jYJ/0/KxZLUhKYkCCuofr8fSWWwxBnynN7Fgi//NbkK+iogIAMHToULef2+12HD9+HABwySWXuD03aNAgAEBtba2/ukXU5VxPVrt2qWHmOYiIqFu4DrxMnGhCdHTnZtx2h9GjmclHREQULkpL5XP5RRdZg35gNdBuu03ObPr00ygYDEIAe9M5FguwcSPX44tUzz5bj8xME4COPrMCMjNNeO65+u7oFhH27lVj3z65ilgoZJAyO5bI//wW5DP/HOnQ691na1VWVsJisSA2NhY9e/Z0e0778xWy3c7ZLxQ6Lr3UDJ1OOlkZjQrs3s2SnURE/iaK7kG+YB14ufhieebHyZMq1NSEzuAWERERuTtwQC7VmZbG0o0dueEGo7PcWmOjAl9+qeugRfAoKtKgoUGuGJGdHXwVI8h/oqKA5curMWdOc4fBiccea4AudD7aFOJWrpTH2S+7zIxhw4I/o5zZsUT+57cgn+7nM1xTU5Pbz0+ePAkA6N+/f6s2DQ0NAFoHBomCmVYLjBkjD+IWFDDIR0TkbwcOqHDihDzQNm1acAb5+va1IzHR5tx2nXVJREREocU1ky81NfgHVgNNrxcxY4bBuR1KJTtdJ5NdcYUJen3wVYwg/4qKAnJz61FcfBaLFjUgO9uIsWPNyM42ok8f+fv/6quxEPnxoG5gMAj45JMo5/bs2c0B7E3nMDuWyL/8FuRLTk4GABw8eBBWq3zy+/777wEAAwYMcNtfFEUcOnQIANCnTx9/dYvIL1zrRBcVcQCXiMjf1q6VB17GjDEjOTk4Z/kJgvu6fCzZSUREFLpcM/lSU5nJ541bb5XLrRUUaHH0aPAvbCeKwNdfB3/FCOoeSUl2LFjQhLy8Gnz+eRXy8mrw6qt1zucLCrTYsoW1e8n/Pv9ch8ZGaSg/JsaO668PnWOTN9mxarWIOXOa8f771cyOJeokvwX5Ro4cCQCor6/H3//+d3z33Xf45JNPsH37drfnHdavX+/M+hsxYoS/ukXkFxMmyKU7ios1sHJSJxGRX4VCqU4HBvmIiIhCX0ODgFOnXMt18qbPG2PHWjBsmHwttHp18GfzlZWpcPy4/LeeOjW4rzWp+2VnmzFxojwO9Je/9GA2H/mdazb0jBmGoFyT3pNzs2MHDZLPoykpFhQXn0Vubj0DfEQ+8FuQb8qUKYiKklKI9+/fj9dffx2fffYZ7HY7evfujVGjRgGQync++eSTWLlyJQBpXb7s7Gx/dSuk2Ww22Gy2jnekbjdunMU5E6W5WYG9ezmIS0TkL6dPK7Bnj5w1PX16cA+8jB4tD2zx/EBERBSaysrkc3hMjB39+/Pe3BuCANx2m5zNl5+vR7APa7hOJrv00uCtGEGBtWhRg/Px7t0afPUVIxPkP4cOqbBjh5wxOnt2i4e9g5sjO3bxYrkkp9kscA0+ovOg6ngX38THx+P+++/HG2+8AbNZLmWo1+tx3333QRCkGrwGgwFnz551Pj937lz07NnTX90CALz99tvYvn07Fi1ahLS0tA73Ly8vx1dffYXy8nIYDAYkJycjOzsbU6dOhULhOU5qs9mwceNGbNu2DadPn4ZSqURKSgpmzJiBlJQUj23tdju++eYbbN26FadOnYLJZIIgCEhOTkZWVhamTp0KjYalIYNBVJSISy81o7hYOuEWFWkwZgzLtxAR+YPrwMugQdagX2zcNch3/LgKtbUCevYMrVmXREREkc69VKcVQkfLCpHTLbcY8MILsbBaBZw5o8Q332hx1VWmjhsGiOu1ZrCu+0yBN26cBdOnG7B2rZTgkJvbA9OnG6EM/oq0FIJcs/hGjLDgkktCf8zRdW3b48dVaG4WQi47kShY+C3IBwCjR4/Gn//8Z2zZsgUNDQ3o06cPLr/8cvTo0cNtP71ejyFDhuC6665DamqqP7uEdevWOUuGemP9+vVYsWIFRJe8+5MnT2LlypXYv38/HnroIahUbf8zms1m/PWvf0VZWZnbz/fu3Yt9+/bhnnvuwcSJE9tsazQa8fe//x2lpaVuPxdFET/99BPy8/NRXFyM3/3ud4iJifH6/ZD/ZGTIQb6CAi3uvz90FsAlIgol55bqDPZBtn79bIiPt6OuTpoYtG+fGllZ5g5aERERUTA5cEDO5ON6fJ2TmGjH1Vcb8eWXUjBk1Sp90Ab5KioU+O47eTJ1sJeFp8BauLAR69bpIIoCysvV+OijKMyaZQh0tyjMmM3ABx9EObdnz24O+ntgb/Tvb4Neb0dLi3SffPCgigkTRD7yW7lOh6SkJPzyl7/EvHnzMH369FYBvqFDh+KNN97A7373O78H+LZs2YK8vDyv99+1a5czwKfT6XDzzTfj4YcfxowZM6BSqbB7926sXr263fZvvvmmM8A3cOBA3H333XjwwQcxevRoiKKId999F4cPH26z7b///W9ngG/06NH43e9+h1dffRVPPvkkhg4dCgA4cuQIli1b5vX7If/KyJAHbHfs0AR9CRIiolDU2Chg+3a5TEmwl+oEpDJVo0fL54i9e5mFT0REFGpKS13X4+MgZGe5luxct06H6mq/D0f5ZP16eTJZ//5Wrr1IHqWmWnHzzXJQ769/7QFTcMavKYR9/bUO1dVSiqhWK7p95kKZQgEMHy4fY8vK/JqLRBTWgvOqyg++/PJLvPvuu24ZeZ6YTCYsX74coihCq9XiySefxIwZMzBmzBjcfPPNuO+++wBImX4nTpxo1X7Xrl0oKSkBAKSmpuIPf/gDsrKycNlll+HRRx/FmDFjYLPZsHz58lZtv//+e+zatQsAcMstt+Cxxx7DxRdfjISEBKSmpuLxxx/HgAEDAADFxcWoqqry6d+EutZll5mhVEqfr4YGhVs5FyIi6hqbNmlhsUjTFuPj7bjsstDIiHMt2blnD9flIyIiCiWiCJSWyudvBn46b9IkE5KTpZmwVquADz+M6qBFYIRaxQgKvN/9rhEqlTQWdPKkCitX6jtoQdQ5rqU6r7vOgPj48ClpOXy4fJ/sep4los4J+yCf0WjEG2+8gfz8fCgUCmRlZXnVbuPGjairqwMAzJgxA4MGDXJ7Pj09HRdddBFEUcT69etbtf/kk08AAGq1GvPnz4daLR+oBEHAzJkzIQgCjhw5gvLycre2X3zxBQBg3LhxuP7661v9bq1Wi0mTJjm3Dxw44NV7Iv+KiRHdBnELC7Ue9iYiIl+4DrxMnWpEOxWzg87FF8vnh717efNCREQUSk6fVqChQR4+cR2UJO8olcDMmXI236pVeng5B7vbGAwCtmyR7+O5Hh95Y9AgG2bPlj/br73WAy0tjA5T1zh1SlrH1CEnp8XD3qGHmXxEXSPsg3xHjhxBcXExoqOj8dhjj+Hyyy/3ql1xcTEAKUjnGlBzNWXKFABwZuw5nD59GidPngQAjB07FomJia3a9u3b11me1JG1BwA2mw2XXXYZrrjiCtx0003t9i8uLs752GLhDUawcC3ZWVjIcmxERF3JYgE2bpSDfKFQqtPBdRLI0aMq1Nfzxp+IiChUuK7H16ePLayyKLqTa8nOgwfVKCkJrolPW7ZoYDRK12g9etjd7u+JPHn44UbodHYAQGWlEkuWRAe4RxQupAkR0nFp0CArMjPD67iUmioH+ZjJR+S7bgvynTp1Ct9++y0+/PBDvPfee86fO3525swZv732pZdeiueffx4jR470an+r1Yrjx48DAIYNGwa9vu1Ue8faeI2NjaioqHD+3HWdvUsuuaTd10lJSWm1v1KpxDXXXIN77rnHWZKzLdXV1c7H0dG8eAgWEybIxdcLCzWw2wPYGSKiMFNYqEF9vXTpotWKuPLK0FnwYsAAG+Lj5ZPCvn28gSEiIgoV7qU6OcnWV4MG2ZCZKV+/rV4dXGUNXStGTJ5sgobzdslLycl2/PrXzc7t//3fGNTVcVIfnR+bDVi1Si5tnJPTEnYlhF0z4ysqlKipCft8JCK/8Ps3p7y8HM8++yyeeeYZLF26FP/5z3+wadMm5/OnTp3Cf/7zH/z+9793lqnsSikpKXjkkUcQHx/vdZva2lpYrdJMgiFDhrS7X1JSErRaKWW6srLS+XPXx45AXlscQTzX/b313XffAZBKfw4fPrzT7ck/xo83QxCkWZ21tUocPMhUcyKiruI68HLFFSZER4fOLHpBAEaNYslOIiKiUFRaKt/XMch3flxLza1ZExU0ZQ3tduDrr93X4yPqjN/8pgk9ekiT+urrFXjzzZgA94hC3ebNWpw+LZ1/lErRreRxuOjd246ePW3ObZbsJPKNX4N8xcXFeOmll3D06NEO97Xb7fjoo4/w5ZdfdmkfXNfC81ZjY6PzcXJycrv7CYKAmBjppF1VVdWqvVKpRK9evdpt36NHDwBAXV2dM6jojWPHjqGsrAyAlKXYmQAm+VdcnIiRI13X5ePUPyKiriCKwNq1oVmq02H0aLm0yp49DPIRERGFCtdMPtfSYtR5115rdAZCmpoU+PxzXQctusd336lRVaUEIA2mT54ceteaFFgJCSLuu6/Juf3OO9GoqGBWEvlu5Uo523naNCMuuCD8yoUJgvt5lUE+It/47ZtTW1uLd955BzabFI1PSkqCUqlsVZYzIyMDNTU1znXpPv74Y4wdO9ZjcM3fXNe4cwTi2hMdHY3q6mrU1ta2ah8dHQ2Fov0TuqPMpiiKqKur8xgQdBBFEe+//z5EUYRCofC4bt+5nn766VY/02g0yM3NBQCvXj/SqVTSVyYpKandfaZMUWDfPunxd9/F4oknWE6ViALPm+NXMPv+e+DUKfmy5bbbopGUFFrH14kTBfzrX9Lj/fujkJTEQB+RN0L9+EVEoc1sBsrL5WuQzMwYJCV5n6HDY1hrOTki3n5bevzRR3F48MHAZzxt3SqP3WRliRg6lOMj1HlPPQUsWyaislKAwaDAO+8k4W9/C93ADI9fgVNRAaxbJ5977r9fHbZ/h0svVaCgQHp87FiPkLvPp+AUaccvv00pWbt2Lcxmacb6L37xC+Tm5mLEiBGt9hsyZAgefPBB5OTkAABsNhs2btzor251WkeZgI4PjMnUel0gb9u2174t69atQ3l5OQDgqquuwoUXXuhVO+o+V1whl4/bskWAGDrV5IiIgtYXX8iXLOPH2xHAuUA+GzNGPiEcOiSgoSGAnSEiIiKvlJUBVqtUUlKlEpGaGuAOhYG77pKvibZtU+DgwQB25meu15rXX8+bePJNjx7AokVyUO/ttxXworgZUSvvv69wnnv69RNx9dXhe1waOVJ+vH9/cJRwJgo1fsvk27t3LwCgZ8+euOWWWzrc/+qrr8bGjRtx9uxZ7HOkQQWIUql0PtZ0sNKyI1DnCGi6tve2LeCePdieY8eO4cMPPwQA9O3bFzNnzuywjasXXnjB4/NVVVUQGZHyyBH997SOYlqaAoA0+nz2rIDCwhqkpNja3Z+IqDt4c/wKZp980guAdH6dMqUJlZVNnhsEodhYIDY2GQ0N0iDSN9/UIzPT3EErIgr14xcRhbaCgigAPQEAKSlW1Nd37ljEY1hrAwYAaWlJOHBAmhj9v/9rxNNPN3bQyn+OHlXihx8ucG5nZlahspL38OSbm28GXn21N06fVsFiEfDMMxb87W91ge6WT3j8CgxRBP797yQ47n9vuaUJtbWBO0b6W79+GgBS9vS+fUBFRSUExvroPIXS8UsQBPTp0+e8foffMvmqq6sBACkpKR5LVroaPHiwW9tA0enkmvAdrZXneN41SOdo721bwD1I2Jbm5ma88cYbsFgs0Gq1+O1vf9thEJECIyHBjtRU+fNQVKQNYG+IiELfqVMK7N0rn/NCcT0+QFpvYNQo+fzAdfmIiIiCX2mpPDnX9T6PfCcIwG23tTi3P/hAjw6GT/zq66/lMaDhwy0YNIgBPvKdTgc89pg8IfHDD6Nw8CDXGSPvFRdr8OOP8r2i6/EyHA0bJp9b6+sVOHOGa1kSdZbfvjWOjDDXgFlHHOUthQCH62Ni5HrwVVVVHvdtbm4GALdApqN9TU0N7Pb2a2872p7b/lx2ux1vvfUWKioqAAB33XUX+vfv77FfFFgZGXLQtrCQwVgiovPhOvAyaJAVQ4cGcBToPI0eLd/A7N3LIB8REVGw++EH+Xydmhq61yDB5pe/bIFGI40bVVQosXFj4CbHrlsnX2tOmxaak8kouMyc2YIhQ6Tjhd0u4OWXewS4RxRKVqzQOx9nZZkwcGB4TzyIjxeRnCy/x9JS3icTdZbfgnzx8fEAOg6SuTp27BgAIC4uzh9d8lpsbKwz4Oip/3a7HfX19QDcg5mJiYkApPUFa2tr221fV1fnfOwpGJqfn489e/YAAK677jpkZGR0/CYooCZMkNdYLCjQcl0+IqLzsHatfI6cPt0Y0qU7Ro+WJ4Ewk4+IiCj4MZPPPxISRLfqDKtW6T3s7T91dQKKiuSJuVdfzSAfnT+VCnjiCXkB7i+/jMLu3bz2p47V1wv44gv5/jcnp9nD3uHD9fxaVsbMV6LO8luQb/jw4QCAsrIyj4Euh9LSUhw/ftytbaAoFAoMHDgQgBx4bMupU6dgNEoXgI7AHgAMGjTI+dhT+0OHDjkfJyQktLnPxo0b8d///hcAMHbsWK/WN6TAc83k++knJY4fV3rYm4iI2tPQIKCgQJ7ZHaqlOh1cy3UePqxCU1MIRyyJiIjCXH29gNOn5cHGESOYydeVXEvQrV+vQ0VF95do27RJB5tNuh7r1cuGMWMYyKWucf31RowaJY8NvfQSs/moY59+GgWjUToW9uxpwzXXhPb9r7eGD5fPr8zkI+o8v11BZWdnA5Cy3f7xj3+gqamp3X0PHz6Mt956q1XbQBoxYgQA4IcffnAG8s5VVlbmfOxaPrNPnz7o2VNamLukpKTd13C0T0xMhF7fetZaYWEhli9fDkAKHN53330BL2VK3und246LLpJvDliyk4jIN5s2aWGxSOe+hAQbLrvM8xq2wW7wYBtiYqRS3qIoYP9+3sAQEREFq7Iy+TwdG2tH377hXTKtu2VlmdC3rzSwa7MJ+PDD7s/mO7dUp4eVVIg6RaEAFi1qdG5/+60O27dzbIg8y8uTj4O33GKANnCVjLsVM/mIzo/fLl9SUlKcZSWPHDmCJ554Avv27XM+v3btWuTn5+PFF1/En/70J2fpyoyMDKSkpPirW14bP348AMBoNGLDhg2tnrfb7fj2228BABqNBkOGDGmzfVFRUZslP48ePYqjR48CANLS0lo9X1xcjLfffhuiKCIxMRGPPPIItJFyZA8T7uvy8W9HROQL11KdU6eaoAzxxGiFArj4YvkGhiU7iYiIgtcPP8gDjcOHW0K6ZHgwUiqBW281OLdXrYrq1qUuzGZpQpkDS3VSV5s82YTx4+XlXP7yl1gu50JuKisVeO21GOTkJOCqq5Kwd68cCM7JafHQMry4rnl78KAKNs6pIeoUv85Ruuuuu5ylN41GIyoqKpzPrVq1Cl999RUOHjzo/Nnw4cNx1113+bNLXhswYACGDh0KAFizZg3Ky8vdnl+zZg1OnDgBAEhPT28VgJs8eTIEQYDFYsFbb70Fk0k+qTc3N2PJkiXO7YkTJ7q1ra2txTvvvAO7XZrpP3XqVBw/fhx79uxp87/Dhw933RunLuMe5ONsLSKizpIGXuQgX7gMvDDIR0REFBpcS4a5DkBS17n1VnkQ+8cf1di5s/vunQsLNWhslIbFdDoRWVmhXTGCgo8gAE8+KWfz7dqlwfr1nAROgMEALFwYh/T0C5CbG4vNm3Vu5xxBELFkSTTaKS4XdoYOtUIQpAi40ajgskdEneTX/FeNRoOFCxdizZo1+Prrr2EwGNrcT6fTYfr06bjxxhuhCKLaCLfeeitefPFFmM1mvPTSS8jOzkZycjJ2796N/fv3A5De44wZM1q1TU5OxpQpU7BhwwaUl5fjmWeeQVZWFkRRxLfffouamhoAwKhRo1pl8m3bts0tKLh69WqP/Rw+fDieeuqp83271MUyMuS/4fHjKpw6pUC/fvYA9oiIKLQUFmrQ0CAPvFx5pamDFqFh9Gg5yLdvH4N8REREwaq0VB4ySUvjWm3+MGCADVdcYcLWrVLgIy9Pj/T07gm2ff21PJksK8uEqCimWFHXmzDBjClTjNi4Ufq8vfRSLK66qpKlYSOYwQDMnZvotvb8uURRwIoV0Th8WIXly6sRFdWNHQyAqCgRF15ow9Gj0nm3rEyNwYOZzkfkLb8XuVUoFLj55pvxi1/8AgcOHMDRo0fR2CjNYunRowcGDRqEtLQ06HS6Dn5T90tJScH8+fPxzjvvwGq1YuPGjW7PK5VKzJ8/H717926zfU5ODurr67Fz505UVlbi448/dns+OTkZ9957b6t2rhmPFLr69rXjwgutOHZM+poVFWnxy1+2HegmIqLWXNdIueIKE/T68Bh4cc3kO3RIhZYWIWzeGxERUbgQRfdMvrQ0ZvL5S05OizPI9/nnOjz3nICYGP9eG4mi+7VmuFSMoOC0aFGDM8h34IAan30WhZtu4vhQpFq8OO7nAJ8IwFMdaBEFBVosXhyH3Nz6bupd4KSmWpxBvtJSFa65JsAdIgoh3baSpU6nw5gxYzBmzJjuesk2paWlYenSpV7vn5GRgf79++ODDz7Anj17IIoiBEHA0KFDMWvWLI/rB6pUKjzwwAPYunUrvvjiC5w9exYAoNVqMXHiRPzqV79CdHR0q3a//vWv8etf/7rT742Cz4QJZmeQr7BQwyAfEZGXRNF9Pb7p08Nn4GXIECuio+1oblbAbhewf7+622asExERkXdOnVI6SzkC0pp85B/XXGNAXFwc6usVaGlR4PPPo/y+FtWBAyqcPCkPiU2dGj7XmhR8Ro2y4oYbDPj8cykd6+WXe+C66wxQs6hHxKmoUCA/X//zVkcLvUrP5+fr8cQTjUhKCu/qYMOHW/Hf/0qPy8r45SDqjG4L8oWy/v3749FHH0VLSwuqq6sRFxeH2NhYr9oKgoCsrCxkZWWhsrISZrMZSUlJ0Gi4RlskyMgwOU/entLwiYjI3f79Kpw+LV2mCIKIadPCZ+BFoQBGjbKgqEg6L+zZwyAfERFRsDlwQB4u6dvXirg4Zt37i04H3HyzAUuXSpOg8/L0fg/yuWbxjRljRu/e4T14ToH3xBMN+PJLHWw2AUePqrB6tR633+7fzzkFn7w8PSyWjoJ77iwWAXl5eixY0OSnXgUH18k0ruWyiahjrADdCXq9HgMGDPA6wHeupKQk9OvXjwG+CJKZKQ/aHj6swtmz/MoREXlj7Vp50YGxYy1hN2vRtWTnnj2cpUhERBRsWKqze+XkNDsf79qlQXm5fwd4XdfjY6lO6g4XXWTDrFlyUO9vf+sBA4s9RZzCQt/GhAsKwn8sOTVVPtcePqyCyRTAzhCFGEYciPxowAAb+vaVT1JFReF/UiYi6grhWqrTwTXIt28fg3xERETBxjWLIC2NpTr9bdQoK0aNkifJ5uXpPex9fs6cUeD77+V7cwb5qLs8+mgjNBopK/jMGSWWLWu9hA+Ft6Ym34bifW0XSoYMsUKtlr4fVquAw4eZzUfkLb8dIaqrq1FdXY26ujp/vQRR0BMEICNDvlEpLGTJTiKijpw8qcT+/XLgKxyDfKNHy4OFBw+qYDB0rmQLERER+deBA/K1iGt2AfmPa4nODz+MgsVPsdX16+XJZAMHWjF8OP++1D369bPjjjvkrNV//jMGjY28D4gkoo+Vn2NiwquyTVvUaiAlRT4ec10+Iu/5Lcj3xz/+EY8//jj+/ve/++sliEKCe5CPmXxERB35+mt5QsTgwVZcdFH4DbxcdJEVer10o2a3C9i/n7MUiYiIgoXZDPz4o3xuTk1lJl93uOkmA7RaaQS8ulrpFozrSq7r8U2bZoTAGAt1owULmhAdLd0H1NYq8fbbMQHuEXWHU6cUeOiheHz3nW/jgq7LAYUzrstH5Bu/Bfl69uwJAFCp+IWkyJaRIReRLitTo6Ym/FPsiYjOh+t6fNOnh+fAi1IJjBwp38Ds3ctZikRERMHi0CEVrFbpAkStFsNywlEwio8X8YtfyIuU+aNkZ0uLgK1b5QllLNVJ3S0x0Y758+VsvrfeiuY4URhrbhaQm9sD2dkX4OOPfTumqdWiW6ZzOHPNrC4rY0yByFt+O4sMHDgQAFBVVeWvlyAKCUOG2NC7t825zXX5iIjaV18vuC0qHo6lOh1cS3bu2cNzAxERUbAoLZUn36SkWKHhabrb3HabPJC9aZMWP/3UtcNWmzdrYTJJAdzYWDsmTIiM7BgKLvfd14T4eCmbr7lZgX/8g9l84cZmkyYqXHFFb7z2Wg8YjfLMVcffHuiodqf0/KxZLUhKCv9ynYB75jzLdRJ5z29BvssvvxwAUF9fj0OHDvnrZYiCniDA7cbBdfCaiIjcbdqkc86cT0y0Ydy48B14GTWKmXxERETByLVEGEt1dq+JE80YMEDK5LDbBXz4Yddm87mW6pwyxQg1L8EoAGJjRTz4YKNze9myaJw+zWy+cLFliwbTpyfh8cfjUVGhdP48KcmGl1+uw44dZ5CZaQLQUckaAZmZJjz3XL1f+xtMXDP5jh1ToaUlDMv6EPmB384gI0eORL9+/QAAq1atgs1m66AFUfhyLdlZWKj1sCcRUWRbu1YeeJk61QSl0sPOIc41k+/gQRUMBg87ExERUbc5cECO/KSmslRnd1IogFtvlbP5Vq3SQ+wo2cVLNhuwfj1LdVJwmDevBRdcII2VmkwC/v73HgHuEZ2vQ4dUuPPOBNx2Wy+384hOJ2LBgkZs3VqB2bNbEB0NLF9ejTlzmqFWt32AU6tFzJnTjPffr4bOP8uTBqUBA2yIipKzFlmyk8g7fgvyCYKA+++/HzqdDj/++CP+9a9/wWwO39n4RJ64LpD7ww8q1NVxJgoR0bnMZqksk0M4l+oEpPJfOp10A2OzCW43gkRERBQ4rufktDRm8nW3WbMMEARp4PvoURUKC7umGk5JiRrV1dIMMpVKxKRJpg5aEPlPVJSIRx6Rs/lWrdLj8OEwnuEYxmpqFHjmmVhMmZKE9evdI3K//GULNm+uwKJFjYiJkQN6UVFAbm49iovPYtGiBmRnGzF2rBnZ2UYsWtSA4uKzyM2tj6gAHyBN9OC6fESd59dc8P79+2PRokVISEhASUkJ/vjHP2Lfvn3+fEmioDRsmBUJCdIMLVEUUFzMkp1EROcqKNCisVG6NNHpRGRnh/fAi0oFjBwp38Ds2cMgHxERUaDV1Qn46Sd5oJ3lOrtfv342XHmlfB24alXXlOz8+mt5tDwjw4y4uC5KESTy0W23teDCC6X7AZtNwCuvMJsvlJhMwJtvRmPixN74v/+Lgc0mT+gfP96E//ynEv/4Rx369Wu/ul1Skh0LFjQhL68Gn39ehby8GixY0BQxa/C1xTXI57pGLhG1z2/h8CVLljgfDxo0CDU1NTh79iz++te/YtCgQejfv7/H9nfffbe/ukbU7Rzr8n31VRQAqWTntGnhPXhNRNRZrqU6s7JMiIoK/4GXiy+2YNcuaeLHvn28gSEiIgq0sjL5fBwba0ffvpE70BpIt97agm++ka4Nv/hChz/9SUBs7PldG7qux8dSnRQMNBrg8ccb8dBDPQEAa9bo8cADTW4TASn4iCLwn//o8PzzsTh+3H1o/cILrfj97xtw7bVGCCzi5RPXyTXM5CPyjt++KVu3bm33uaNHj+Lo0aMe2zPIR+EmI8M1yMdMPiIiV6LoPvAS7qU6HUaPNgOIBgDs2cNzAxERUaAdOCAPk6SmWjhIGyDTpxvRs6cNtbVKGI0KrFkThblzWzpu2I7Dh5UoL5cDuNOmRca1JgW/GTMMeOONGGfGUm5uLJYtqwlwryJPZaUCK1fqUVioQVOTAjExdmRmmpGT0+KWVffdd2o8+2wsiou1bu1jY+14+OFG3HVXM7Tac387dYbrWriuE2+IqH1+LddJRLKMDDlzb+9eNZqaeLdIROSwb5/aWRpLEERMnRoZAy8XX+w+S9EYGW+biIgoaLmvx8dsmkDRaoFf/tLg3D7fkp2upTrT0iwYOLD98nlE3UmpBBYtanBur1+v4xIv3chgABYujEN6+gXIzY3F5s06lJRosHmzDi+9FIv09AuwcGEcDh9W4qGH4nH99UluAT6lUsRddzVh27YK3H8/A3xdYfhw+R757Fklamo4fkrUEb9l8r388sv++tVEISktzYq4ODvq6xWw2QTs3KnhQt9ERD9zLdU5bpwlYtYgGDbMCp1OhNEowGoVUFqqxqWXcu0fIiKiQHFd/4fr8QVWTk4LliyJAQB8/70GBw6ofA68ugb5mMVHwWbaNBPGjjWjpEQK7v3lLz3w4YfVzCT2M4MBmDs3EQUF7UfmLBYBK1ZEY+VKPUTR/Q8ybZoRzzzTgJQUTgjpSr172xEfb0ddnZSbdPCgGhkZ5gD3iii4+S2Tr1evXuf1H1G4USqB9HT5pFRQwJlZREQOrkG+SCnVCQAqlTSb3GHPHpYjISIiChRRBEpL5bnQzOQLrLQ0Ky65RL6H9jWbr6ZGwI4d8v031+OjYCMIwJNPytl8hYVafPstU8L8bfHiuJ8DfB2v9+ka4BsxwoJVq6qwdGkNA3x+IAjuk2xcz8tE1DaW6yTqRpmZcuZeYSEv2IiIAODECSV++EEObl19tcHD3uHHtWTnvn0M8hEREQXKyZNKNDXJwySuJcMoMG67TV6H76OPomDyoRjOpk062GzSAH3v3jZccgn/rhR8Jk40IytL/oD/5S89IHYceyIfVVQokJ/vmDjgbcqkiP/5nzr897+VyMpiZpk/DR8uB09dM+yJqG0M8hF1I9f08t271TAYWHuBiGjdOjmL76KLLEhJiaw1UkaPZiYfERFRMDhwQM4W6N/fithYjrAH2k03GaDTSWXca2uVbteN3nJtM22aEQqOhFGQcl2bb+9eDf7zn85/3sk7eXl6WCydHZMTYDAooFT6pUvkwnWSTVkZM/mIOsJLG6JuNGqUBdHR0g2KxSJg1y4O5hIRRWqpToeLL5YngJSWqn2aoU5ERETnz309PpZgCwaxsSKuu06+Ply9unMlO00m4Jtv5Co6XI+PgtmYMRb84hdyVZOnnorDbbcl4IYbeiEnJwGvvx6DykoO5XaFwkLfltDh0jvdw/UcXFamZlYrUQd4ZiDqRiqV+7p8LNlJRJGurk5wu8G6+urIi3ANH26FVivdtVgsAsrKOAGEiIgoEFzX/XFdD4gCy7Vk5zffaHHqlPdDWYWFWmcJVp3OjiuuiLxrTQotCxY0wrFGXE2NElu26FBSosHmzTq89FIs0tMvwMKFcTAyXn1eamp8GxJ3LelM/uOayVdfr8CZM/x3J/KE3xCibuZastPXmUNEROHCdY2UxEQbxo6NvLUN1GogLU2+idm7l0E+IiKiQDhwQD4Hp6Uxky9YZGaaMWiQ9PcQRcFlHa2OuZbqvPJKE6Kiurx7RF3GYACeey4OntaIs1gErFgRjdtvT4QhspYyP292uzRRYN68BJ/XQo+JsXdxr6gt8fEikpPlZTw4EZbIMwb5iLpZRoY8c7CkRMOybEQU0VxLdU6bZozY9Q1GjeK6fEQUmSorFXjttRjk5LAcGQWWyQT8+KOcyec6AYcCSxCAW2+Vs/lWr9bD7sU4uygC69bJ1XOuvpqpTxTcFi+OQ0GBFo5MvvaJKCjQYvHiuO7oVsirqxPw9tvRyM7ujTlzEvH11zp4CqR6kpkZeZNSA8U1o941056IWuOdE1E3u+QSi3PhcJNJwPffM5uPiCKTyQRs2iQPvETienwOo0czk4+IIovBACxcGIf09AuQmxuLzZtZjowC69AhlbO6gFotYsgQZvIFk5kzW6BQSIGPEydU2L694/vo/ftVOH1aGhgWBBFXXcUZthS8KioULlmqHQWgpOfz8/WcFOPBvn0qPP54HMaNuwDPPhuHI0fODRR1bqE3tVpETk5LxztSlxg+3H1dPiJqH88ERN1MowHGjZMHc7loLxFFqoIC9zVSsrIid1aka5DvwAE1zJH7T0FEEcBgAObOTcSKFdGwWNoeyGQ5MupupaXyAGJKihVqjicGlT597Jg0SQ7SrVrVcclOKVtHMnasBUlJLLNHwSsvT9/uObE9FouAvDzvy9dGApMJ+PjjKNx4Yy9Mn94beXnRMBrl4W9BEDFtmhHvv1+N2bMdAbuOMycBYNasFh5HupHrunxlZczkI/KEQT6iAMjMlG9OCgu1HvYkIgpfrqU6pTVSOjeTMpwMH26BWi29f7NZwMGDvIkhovDFcmQUjNzX42OpzmDkmkHz5ZdRqKvzHBBxXY+PpTop2BUW+jYBnBPHJadOKfHiiz2Qnn4BHnqoJ3btcv936dnThgceaERBQQWWLq3B5MkmPPdc/c/jcx1nTmZmSvtT90lNdc3kU3lVppkoUvk8grRmzRq37RkzZnh8vrPO/X1E4SQjQ07R2LlTDYsFnClKRBFFWiNFHniJ5FKdgJTlnZpqwd690s3o3r0ajBrFMmFEFH58LUf2xBONnD1PfuW63k9aGs/BwWjqVCMSE22orlbCZBLw6adRmDev7dJ5p08rsGePPMjPIB8FO0eFk+5qF4wqKxVYuVKPwkINmpoUiImxIzPTjJyctjPo7HZg61Ytli7V4+uvdbDbW19XjBljxrx5zbj+egN0OvfnoqKA5cursXhxHPLz286kVKtFzJrVgueeq2/Vnvxr6FArBEGEKAowGhU4dkyJwYNtge4WUVDyOcj36aefum2fG5Q79/nOYpCPwtmYMWZoNCLMZgEGgwJ79qjdSngSEYW7PXvUOHNGCUAqmTJ1KtdIGT1aDvLt2aNGTk6AO0RE5AfnU45swYImP/WKyD2TLzWV92bBSKMBfvUrA95+OwaAVLKzvSDf+vXyaPygQVYMHcrALQW3mBjfJrL42i6YGAxoN9C2ebMOr77awy3QVl8vID9fj/fei8bhw62HtnU6ETfd1II772xxWxahLVFRQG5uPZ54ohF5eXoUFHgXYCT/0+tFXHihDUePSn/jsjI1g3xE7WAtKKIA0OmkQF9RkVSqs7BQyyAfEUUU11Kdl11mRmIib5wuvlg+D+zdy/RuIgpP51OObMGCLu4M0c9qawXn5COAQb5glpPT4gzy7d2rwb59qjarH7iuxzdtmhFC5+YWEHW7jAwzNm/ufKpYZmZoL+btWKdXKuPdNsc6vXv3qpGWZsFnn0XBYGidwThokBVz5zbj1ltb0LNn55aCSEqyY8GCJl5rBJnhwy3OIF9pqQrXXBPgDhEFKZ+DfIsWLTqv54kiXUaGa5BPgwceCHCHiIi6EUt1tuY6y/SHH1jKmYjCE8uRUTAqLZVPuHFxdvTpw8lHwWrYMCvGjjWjpESaMLBqlR5//nOD2z7NzQK2bpUDBizVSaEgJ6cFf/tbj05lu6vVottalaHIfZ1eT+9dxJ49GrcyvIBUFeaqq0yYN68ZV15pgoKXC2Fl+HAr1q6VHpeV8eaYqD0+B/lSU1PP63miSJeRYcJrr/UAAOzYoYHNBiiVHTQiIgoDx48r3UpiceBFkppqgUolwmoVYDIJOHhQhZEjWVqKiMJLJJcjo+Dlvh6fhVlfQS4np8UZ5PvkEz2eeabBba2sb7/VwmyW/ojx8Xakp4d2phNFht697Zg1qwUrVkSj44CX5JJLzCFdStKXdXodeva0ISenBXPntmDgQJZwDFeumfVlZSxISNQezm8gCpDLLpMGcwFpZvL+/ZyRQkSRwTWLLyXFgosu4k0ZAGi10kxFh337eF4govCTkeHbYHuolyOj4Oa+Hh8n2AS7G24wICpKCmzU1SncysAD7teaU6YYWRmBQsazz9YjM9MEbwJ8ALBrlwabNrVf5jLY+bJOLwBcd50BO3eexe9/38gAX5hzPSf/+KMKZl4OErWJQT6iANHrRbfSbAUFvq1PQkQUalwHYliq093o0fJdy7mlaIiIwkFOTgvU6s6tkxMO5cgouLmW6+R6fMGvRw8RN9wgX0Pm5UU7H9tswPr1ctBj2jRea1LoiIoCli+vxpw5ze2eK1UqEXq9FOQWRQG/+U1PlJeHZoaTr+v0NjYKbtm7FL4GD7Y6vwtWq4DDh0Pzs07kbwzyEQWQNENL4uvFDRFRKKmtFVBUJB/vWKrT3cUXywOLe/Zw2jkRhR9HOTKJd8G+WbNaQrocGQU3u929XCeDfKHBNfC/ZYsWJ05Ia1/s2qVBba30WK0WMWmSqc32RMEqKgrIza1HcfFZLFrUgOxsI8aONSM724hFixqwc+dZfPJJlTObtbFRgTvvTEBNTejVGeY6vdQRjQa46CI5m48lO4naFpHfjLfffhvbt2/HokWLkJaW1uH+5eXl+Oqrr1BeXg6DwYDk5GRkZ2dj6tSpUHSwoqvNZsPGjRuxbds2nD59GkqlEikpKZgxYwZSUlK87rPdbkdubi5KS0vx8ssvIykpyeu2FLwyMsx44w3p8Y4dWtjt4CLBRBTWNm7UwWaTbkB79bJh7FgOpLlyzfD+4QcVrFZAFZFXa0QUzp59th6HD6tQUOBdibHERAb4yH9OnlSiuVm+CWO5ztCQnm7GkCFWZ1ZHfr4ev/tdo1upzsxME2JjO5c5TBQskpLsWLCgCQsWtP3cP/9Zh7vvTgAAHDumwr33JmDlympoQmj+ONfpJW8MH25xZtwfOKDGjBmcKEx0rogLJ6xbtw7bt2/3ev/169fjhRdeQElJCRobG2G1WnHy5EmsXLkSr732GqzW9m8AzGYzcnNzsWLFChw9ehRmsxkGgwF79+7F888/j23btnndj1WrVqG0tNTr/Sk0pKeboVBINx11dQq3GaREROHItVTntGlGTmw4R1qaBUqldF4wGhU4dIjnBSIKP1FRwF/+UgdPmXyCID/3+us9sHp1lP87RhHJ9R5swAArevRgUCgUCAJw221yNt877+hx220JePddvfNnl1/OLD4KX9dcY8RTTzU4twsKtHj66TiIIXQI4zq95A3XdeuZyUfUtogaWtuyZQvy8vK83n/Xrl1YsWIFRFGETqfDzTffjIcffhgzZsyASqXC7t27sXr16nbbv/nmmygrKwMADBw4EHfffTcefPBBjB49GqIo4t1338Xhw4c77MeaNWuwbt06r/tNoaNHDxGjRslZG4WFobtgMhFRR0wm4Jtv5OMcS3W2ptMBw4bJNzEs2UlE4eqTT/QApMzu+HgbsrLcy5F9800Fhg6Vr5MXLozHt9/yWpm63oEDruvxMYsvlNxwQ4tzQkBDgxJbtuhgMsnDXK+8EouFC+Ng5CUnhakHHmjCr34lB7vz8qLx739He2gRXKQlbLhOL3nmem4uK+P9MVFbIibI9+WXX+Ldd9+F6OWUFpPJhOXLl0MURWi1Wjz55JOYMWMGxowZg5tvvhn33XcfACnT78SJE63a79q1CyUlJQCA1NRU/OEPf0BWVhYuu+wyPProoxgzZgxsNhuWL1/ebh/sdjtWrFiBTz75xId3TKHCdeZSQUEI1VUgIuqk7du1znJYUVF2ZGVxdnVbXEt27t3LmxgiCj9WK7BqlZxt88ADzVi1qgaff16FvLwaLFjQhJQUG5Yvr0FSku3nNgLuvbcn9u/nDG7qWu5BPpYRDxUGA/DYYz0hiu2vQ2a1ClixIhq3354Ig6EbO0fUTQQBePnlOlx2mTyu9Kc/xWLDhuCfFFNaKpUYdUz46Zg0nst1eiPP8OHyufnYMRVaWkJv/Ukif/M5yHfy5EmsWbMGa9aswenTp7uyT13KaDTijTfeQH5+PhQKBbKysrxqt3HjRtTV1QEAZsyYgUGDBrk9n56ejosuugiiKGL9+vWt2jsCc2q1GvPnz4daLd84CIKAmTNnQhAEHDlyBOXl5a3a19fXIzc3F19//TV0Oh0yMzO9fMcUaqSZS5KiIk1IlVYgIuoM11KdV15pQhQrr7Vp9Gj5Jn3PHk7+IKLws3GjFmfOKAFIM/JnzWp7Rv6AATa8914NoqKkwbymJgXuuCMRp05FzFxV6gau5TrT0hjkCxWLF8f9vK5nRzfQIgoKtFi8OK47ukXU7bRaYMmSGvTrJ2U72e0CfvvbnkFd1nDvXjVuuSURlZXKTrQSkJlpwnPP1futXxScBg60Oa8FAeDgweD9bBMFis93Rxs2bMCnn36KL774Aj179mz1/LZt27Bt2zavylH605EjR1BcXIzo6Gg89thjuPzyy71qV1xcDEAK0k2aNKnNfaZMmQIAzow9h9OnT+PkyZMAgLFjxyIxMbFV2759+yI1NRWAlPV3rj179qC0tBSJiYl46qmnMGLECK/6TaEnPd3sLDFSXa3k+ktEFJbsduDrr+UgH0t1tu/ii+UBxv37VbDZAtgZIiI/WL5cLiU2fboRvXq1PyN/9GgL3nyz1rmO9ZkzStxxRyIaGjiLm86fyQQcPuwa5GO5zlBQUaFAfr4jG7ijY4H0fH6+HpWVnCBA4alXLzuWLq1BdLQ8KWbevARUVwffZ37nTjVmzUpEba0U4FMoROTm1mLOnGao1W0H7dVqEXPmNOP996uh07W5C4UxhYLr8hF1xOejvWOtucGDByOqjan477zzDt555x1s377d9951kUsvvRTPP/88Ro4c6dX+VqsVx48fBwAMGzYMer2+zf2GDh0KAGhsbERFRYXz566BzUsuuaTd10lJSWm1v4MgCMjKysKf//xnXHjhhV71m0JTz56iW31pluwkonC0Z4/ambWhUIiYNo2lOtszYoTVOZhtMCjw44+8iSGi8HHqlBKbNsllxG6/vbnDNlOnmvDCC/LM/dJSNebPT4DZ7KERkRfKy1Ww2aQgkEYjYvBgBvlCQV6eHhZL5wL9FouAvLy2x3aIwsGIEVb885+1zknkx4+rcM89PWEKotuu7ds1yMlJREODNBytUon4179qMWeOAbm59SguPotFixqQne2+Tm9x8Vnk5tYzwBfBXIN8paVc0oLoXD4H+aqrqwEASUlJXdYZf0hJScEjjzyC+Ph4r9vU1tbCapUOHkOGDGl3v6SkJGi10g1qZWWl8+eujx2BvLYMGDCg1f4OEyZMwN13391mAJXCj2vJzsJCBvmIKPy4lupMTzcjIYHrKLQnKkrEsGHyTcyePbyJIaLwkZend66hNWiQFRMnehepmzu3BQ8+2Ojc3rpViyeeiGepezovruvxpaRYoeYpNyT4es/MCbUU7q6+2oRnnmlwbu/YocVTTwXHufKbb7SYOzcRLS3SULRGI+Lf/67BDTfIFV6SkuxYsKAJeXnu6/RyDT5yXZePmXxErfkc5HMEwTSa4L5IUvtwld7YKN88Jicnt7ufIAiIiYkBAFRVVbVqr1Qq0atXr3bb9+jRAwBQV1fn/Pc8n35T6MrIkAc3ioq0QXEBRkTUldatY6nOznAt2ckgHxGFC6sVbpk0s2e3QNGJO9JFixpx003y+n0ffqjHX//aoyu7SBHGNRuA6/GFjqYm34ayfG1HFEruu68Zt94qnytXr9bjrbeiPbTwv7VrdbjrrgQYjdIkH53OjmXLanD11UGUZkhBzbUCGjP5iFrzOfSt1+vR1NSEurq6LuxOcLBY5It7RyCuPdHR0aiurkZtbW2r9tHR0VB4uGuNjpZOsqIooq6uzmNAsCs8/fTTrX6m0WiQm5sLAH5//XCgUklfma7OYL32WvnxmTNKNDQkwUMSKBFRp/nr+OXJ2bPAu+8qsHatgNJS+Xw4daoeSUksl+TJ5Zcr8MEH0uPSUj2SkrSeGxCFsUAcv8g/Pv9ccJZuVqlE/OY3UUhK6lzlkuXLgeuus2PzZum88re/9UBaWhTmzeMsOeq8w4eVzseXXab1y3GGx7Cul5Cg7HinNtup+HegiPDOO8CpU3Zs3SqdK//851iMGRON66/v3LmyK45f+fkC7r1XCatVCvDFxIj49FM7srNjff6dFHkuv1x+fPasEgpFEhITA9cfCn6Rdv3l8zSmwYMHAwD27dvX5ppy4aKjjDrHB8bURpFrb9u2154iR+/eQGqqfLG1ZUvn1hcgIgomBgPwm98oMWSICosXK7F9u/vlxtSpKvz2twoYmdDXrrFj5XPC998LsNkC2Bkioi6yZIl8PpgxQ8QFF3T+d2i1wAcf2NyunX/zGyXWreP1M3Xevn3y52bUKAaKQ0VWlm9/q+xs/o0pMmi1wOrVNgweLH3mRVHAHXcosWdP9/bjvfek13UE+OLjRXz1lY3fReq0Pn2Anj3lz80PP/C6j8iVz5l8V155Jfbu3Qur1Yo///nP6Nevn7N0pauSkhKcOnWq079/0aJFvnbtvCmV8qywjsqROgJ1ZpdV3x3tvW0LuGcP+ssLL7zg8fmqqiqIrBPpkSP639Y6iucrPT0OpaVSdue6dWZcf31dl78GEUUufx6/XBkMwNy5iSgoaH+ii8Ui4J13lNi/34rly6vB5Wdb69tXgEKRDLtdQHOzgB07apGSYu24IVEY6q7jF/nXqVNK/Pe/vZ3bt9xSg8pK79bja8uyZUrccEMvVFQoYbMJuPVWBT7+uAqjRvFYSd6pqRFw+nQf53afPlWorOz6dZ94DOt6N96owJ//fAEsFu8HedVqETfeWOmXvzFRsFqyRIUbb+yFpiYFmpoEzJgh4D//qfJ6jbvzOX4tW6bH00/HO7d79rRh1apqDBliBQ+H5IthwxJRVCRVuCksbEJqaksHLSiShdL1lyAI6NOnT8c7euBzkG/cuHGYOHEitm3bBlEUcfLkyTb3q62tdStlGQp0OnndoHPXyjuX43nXIJ2jvbdtAfcgIUWmzEwTli+XgnxFRcG91iURUXsWL45DQYEWgAjA08CLiIICLRYvjkNubn039S506PUiUlKsOHhQCpbu2aNmkI+IQlpenh52u3ReuPBCK6644vzuf/r3t2HZshr86leJaGlRoLlZgTvvTMRnn1WiXz8O4lPHXNf0iY+3IzmZn5tQ0bu3HbNmtWDFimh4c80JCJg1q8XrwAZRuBg+3Ip//asW8+YlwG4XcOqUCvfck4DVq6vgMvTZ5d5+OxrPPhvn3E5KsmH16moMH877GfLd8OFWZ5CvrIzr8hG5Oq9Vh++55x7ccccdGDRoUIdZa6HENSOxqqrK477Nzc0A4Lb2nqN9TU0N7Pb2LyIdbc9tT5FpwgR5oOPkSRVOnvRtnQEiokCpqFAgP9+x1l5HM6ul5/Pz9ais5DmwLRdfLE8g2rOHNzFEFLqsVinI5zBnTgu64vZn9GgL3nyzFgqFVI3kzBkl7rgjEQ0NLOFEHXMN8qWlWSDwYxNSnn22HpmZJnhzzZmZacJzz3FSGUWmq64y4Q9/aHBu79ypwcKF8fBXIa/XXotxC/D16WPDRx9VMcBH5y01Vb4/LivzOW+JKCyd9zdi8uTJmDx5cquf33XXXQCAq666Crfffvv5vky3io2NhVqthsVi8Rjks9vtqK+XLhRds/8Sf17502azoba21rl9rrq6OudjnT+n0FBISE62Y9AgK44elb6WBQUazJxpCHCviIi8l5en71TZJEAq3ZmXp8eCBU1+6lXoGj3ago8+kh7v3csgHxGFro0btThzRprAplKJmDWr68orXXWVCS++WI9Fi+IBSIGb+fMTsHx5NcJoHir5QWmpPBziOnBIoSEqCli+vBqLF8chP7/ta1C1WjrePPdcvV+zloiC3fz5zSgvV2HlSql61Ecf6TFsmBUPPth192CiCLz0Ug/84x89nD8bONCK/PxqDBjABcbp/KWmyoHisjI1RBGcoEP0M06db4NCocDAgQMBAMeOHWt3v1OnTsFoNAKAWyBv0KBBzsee2h86dMj5OCEhwdfuUhiRZiJKCgs5KkFEocXX41ZBAY93bXHN5Nu3Tw0PxQGIiILa++9HOx9Pn27s8pJ5t/8/e/cdHlWV/gH8e6fPpEFCAiwgiFRFmoCJ9CoqTQPBELDsrq6uiq6uou66CGsja8NddnUt608IgYQiqBSlBjAgAoIgTTqhpNfpM/f3xzVzJ5I6mWTa9/M8Ps5k7smcCTNn7j3ved8z04jHHy9z3d+5U4s//7npshQoOPz0k7yAxn3ikAKHXg+kppZg796rmDOnFMOGmdG/vxXDhpkxZ04p9u69itRUBviIBAF49dWSKnNOr78eifXrvfPhEEVg7tzIKgG+G26wYdWqfAb4yGu6dZOvj4uLFbh6lWENokr8NNTgxhtvBAD89NNPrkDerx0/ftx1u3379q7bbdu2RcuWLQEA+/fvr/E5KtvHxMTAYDDUeByFjvh4uWTn7t1aH/aEiKjhyss9O63wtF2w69XLBkGQZqjLyxU4c4ZlnIko8OTkKLF1q3xeO3NmRS1He27OnDLcfbecIbhypQFvvhlRSwsKZU5n1VJfPXsyky+QxcY6MXt2OdLTC/HFF/lITy/E7Nnl3IOPyI1GA/z3v4Xo1Ele1PDEEy1w+HDjirw5ncCcOVH4+GN566OePW1YubIAbdvyM0je07KliDZt5KAx9+UjknFWrQaDBg0CAJjNZmzevPmax51OJ7Zv3w4A0Gg06Ny5c7Xt9+zZU23Jz7Nnz+Ls2bMAgJ49e3qz6xTA3IN8Z8+qcOUKP6JEFDjCwz27iPO0XbALCxNxww3yRfiPPzLjkYgCz7JlBjidUi2ljh3tGDLEWkcLzygUwFtvFVfJUnj33YgqewESVbpwQQmjUb7W4l5RRBQKoqNFfPppISIipOsvk0mBBx+MRm6uZ3NPdjvw1FMtkJYmZ+z37m1FRkY+g+zUJLp3lxflHD3KffmIKjVZBKFbt27o3r07Wrdu3VRP0aQ6dOiArl27AgDWrFmDkydPVnl8zZo1uHDhAgBg4MCB0GqrZl2NHDkSgiDAZrPhgw8+gMUiX2xWVFTg448/dt0fPHhwU70MCjDt2zvQvr18gclsPiIKJO4LFRoiIaFpJnyDQe/e8kXMoUNcqUhEgcVuB5YulYNsM2YYoWjCNWxaLfDRR4Xo2lUeO+fMicK2bTynpqqOHZO/U6+7zo7wcNZ2JaLQ0LWrHe+/XwSFQhr3Ll1S4be/jUYNRcxqZLUCjz3WEitXyt/zAwZYsXx5AaKjOaZS03BflMNMPiJZk11ivfDCC3j++ecxduzYpnqKJjd9+nQolUpYrVYsWLAAn332Gb7++mv84x//wJo1awBIWXyTJ0++pm2bNm0watQoAMDJkyfx17/+FWvXrsWaNWvw0ksvuQKEvXr1YiYfVVG1ZCezNogocCQnG6FWN+yCTq0WkZxsrPvAEOW+Lx+DfEQUaLZs0eLKFanUsEolYvr0ph/vW7QQsWRJIeLipHJODoeAhx9u2ehyZBRcfvpJfj/06MFSnUQUWkaMsGDevFLX/QMHNA3ay9ZsBh5+OBpffql3/ey22yxYurQAkZEM8FHTcf/Odi+7TRTqWAuwFl26dMFDDz0ElUoFu92OLVu2YOnSpThy5AgAQKlU4qGHHkJcXFy17ZOTkzFgwAAAQF5eHlatWoXVq1ejsLAQgBQIfPjhh5vnxVDAcC8xxCAfEQWSuDgnkpIqJ3DruriTHk9KMrKUSy3cg3yHD6vh5J+KiAKIe/mu2283N9t43769A599VgiDQXq+igoF7rsvBjk53NuUJO6ZfD17slQnEYWeBx+swKxZ8j65q1cb8N574bW0kJhMAh58MBrffKNz/WzkSDM++6wAYWEM8FHTqprJp+L1MdEvhIqKCo7Adbh48SIyMzNx6NAhiKIIQRDQtWtXJCUloUuXLrW2FUURO3fuxJdffomrV68CALRaLQYPHozExESEhYXV2r45GAxSav3ly5ch1nfZToiKjY0FIAVtm8qZM0oMGSKXuT148ApateK3FhE1TnOMXwBgMgEzZ8bUq9xwQoIFS5YUQKer89CQVVYmoEePtq77O3dexfXXO2ppQRR8mmv8Iu/KyVEgPr61az++9PQCDBtmqaOVd23ZosUDD0TD4ZD60L27DatX5yMqitc8oW748Fj8/LMU6PvPfwoxaVID69Q1AMcwIvJXNhuQkhKDXbvka7f//rcQgwZZsXSpAfv3h6OsDNBqrUhIsGLSJBOefroF9uyRj7/jDhMWLSqClpWxqRkYjQK6dWsDUZTO7XbtuopOnXh9TNcKpPMvQRDQtq0072M0elb5hEG+BjAajSgoKEBUVBQiIyMb3D4vLw9WqxWxsbHQaPwnQ4tBvvprjgFCFIEBA1q7Sht98EEhJkxouotOIgoNzXmCs2+fCpMmVZ/lDkglOpOSjJg/v4QBvnoYOjQOp09LpUj+/e9CTJ7M7wQKLYF0gUayt96KwNtvRwAAOna0Y+fO3Cbdj68mS5YYMGdOC9f9wYOlBSZ+dDlGzcxsBrp2besKQG/blouuXZsum49jGBH5s6IiARMnxuLMGel6Q6kUIQiA3S5cc6wgiK7gCgBMmWLEu+8WQ81dBagZDR4ch7NnpffrJ58U4vbbeX1M1wqk8y9vBPlYrrMBDAYDOnTo4FGAD5DeXO3atfOrAB/5H0EA4uPlVc579vD9QkSBZds2eW+G3/zGjmHDzOjf34phw8yYM6cUe/deRWoqA3z11bu3vFfrjz/yO4GI/J/dDqSnG1z3Z8ww+iTABwAzZxrxxBNlrvu7dmnx5z+3QG6uAgsXhiM5ORoTJ7ZCcnI03nsvHHl5vEQOdidPql0BPq1WxPXXs1wnEYWuli1FfPppASIjpQpSDodQbYAPQJUAX2KiEe+9xwAfNb/u3eUtLY4d4758RADATwKRH4qPt+Lzz6WJkexs1jwgosCyfr0cvXvgASMee6zch70JfDffbMPnn0u3Dx3iVTQR+b+tW7W4fFmqSqFSiW77tfrGnDlluHhRidWrpfPrlSsNWL1a7wr0VMrK0uHttyOYbR7kjh6Vp0G6drVBxVkRIgpxXbo4MGCAFVu21P+LT6cToeRWt+QD3bvbsXGjdPv4cX6JEwHM5CPyS/HxctbGsWMqFBVVv4qKiMjfnDunxNGjciBq/HiTD3sTHG6+WV6pePiwGqysTUT+bskSed/xcePMiIvz7f7SggC89VYxBg2Sq2X8OsBXyWYTkJYWhpkzY2DiV1hQOnZMPk/p0YNZfEREubkK7NjRsAXmGRkGZr+TT/To4Z7Jx0WwRACDfER+qUsXO1q1kjaOFUUB333HbD4iCgwbNsirP7t1s+GGG7gJdmO5B/lKShQ4f55LZonIf+XkKLBli3zuOmuWb7P4Kmm1wHXX1fc7SUR2thZz50Y1aZ/IN9xLe/XsaavlSCKi0JCeboDN1rDF5TabUKU0N1Fz6d5dXqBz6pQKVmstBxOFCAb5iPyQIAC33ip/S+3ezT2YiCgwuAf5uAG2d0RGiujUSb6QYclOIvJny5cbXFly111nx5AhljpaNI/cXAXWrNHXfSAAQOo/sxSCk3vFAWbyERF5PueUnc25Kmp+nTvboVJJ5W3sdgGnT7NkJxGvWIj8lHvJTgb5iCgQ5OUpsHevPF7dcQeDfN7Su7ecafDjjwzyEZF/stuBpUvlUp0zZhih8JMrTmYpEAAUFiqQmytnxLuX/CIiClXl5Z59WXvajqgxNBrghhvkRTrcl4+IQT4ivxUfL696PnxYjdJS7stHRP7tm290EEVprGrb1lElMEWN416yk0E+IvJXW7dqcfmyFEBRqURMn+4fpToBZimQ5OhReSKwZUsHWrf27X6RRET+IDzcs7HQ03ZEjeVespP78hExyEfkt3r0sCMyUjphcjoFTJjQCsnJ0XjvvXCWDSIiv7R+vVyqc/x4EwSuTfCam2+Ws7sPHdJAFH3YGSKiGqSlyVl848aZERfnP5N/zFIgoOpEYI8edp6rEBGhaiWphkhI4GZo5BvumfjM5CNikI/IL5lMwPPPR6GsTL7qPHVKjawsHRYsiMTAga3x3HNRMLMSHhH5ibIyATt3al33x4/nAOVN7pl8xcUKXLyorOVoIqLmd+mSAps3y98DM2f6TxYfwCwFkrhn8vXsyYoDREQAkJxshFrdsFWEarWI5GT/+q6n0OG+p+7x48zkI2KQj8jPmEzArFkxSEsLc5W9+zWbTUBaWhhmzoyBydTMHSQiqsbWrVpYrdKY1aKF0+PVoFS9Fi1EdOwoX8gcOsQLGSLyL8uWGeB0St8D111nx9ChljpaNC9mKRBwbSYfEREBcXFOJCVVBuzqCvZJjyclGREby4Uw5Bvdu8sLdc6dU8JoZGo+hTYG+Yj8zNy5UcjO1qI+J1bZ2VrMnRvVHN0iIqrVhg1yqc4xY8xQsWKG17ln8zHIR0T+xOEAli6VS3XOmGGEws+uNJmlQE5n1ZJezOQjIpLNm1eChAQLgLqCJQISEiyYP7+kObpFVK3rrnNAp5OCzKIo4MQJTkBQaPOzSy+i0Jabq0BGhuGXe3WfWAFARoaBe/QRkU9ZLMDmzXKQ7447WKqzKbgH+Q4fZpCPiPzH1q1aXL4slRFWqURMn+5/gTFmKdD580oYjfJ1U/fuzOQjIqqk1wOLFxcgJaWixkUxarWIlJQKLFlSAJ2u2kOImoVCUfV7nPvyUajjJ4DIj6SnG2CzNSzF3GYTkJ5uwOzZ5U3UKyKi2n37rRbl5dKkmU7nxPDh/lWiLVj07l01k08UAYFVSYjIDyxZImfxjRtnRlycfwbG5s0rwenTql+qZtSGWQrB6OhReYFMx452hIU1LLOTiCjY6fVAamoJnn22DOnpBuzbF46yMkCrtSIhwYrkZC5+If/RvbsdBw9qAFSW4+Z+RhS6GOQj8iO7d2s8apedrcHs2V7uDBFRPa1fLy/jHDHCAr2ek2ZNoVcveV+owkIlLl1Sol07hw97REQEXLqkwObNctBs5kz/y+KrVJmlMHduFDIyal5cd8stFmYpBKFjx+Tpjx49WKqTiKgmsbFOzJ5djthYPQAgL6/Qxz0iupb7vnzM5KNQxxp/RH6kMhOmudoRETWWwwF8/bU8Czp+PEt1NpXoaBEdOsglSbgvHxH5g+XLDXA6pWBZhw52DB3q39nclVkKe/dexZw5pRg2zIz+/a1VxlezWcEAXxByz+Tr2ZOlOomIiAJZjx7u5Tp5bUyhjZEBIj8SHu5Z2QNP2xERNdb+/Wrk5Un7MCmVIsaMYZCvKbnvy8cgHxH5msMBLF1qcN2fMcMIRYBcYVZmKaSnF+KLL/KxeLGcpXDkiBpHjnBFeLBhJh8REVHwcM/ku3JFiaIi7mVBoStALsGIQkN8vLXug6qRkOBZOyKixtqwQe+6HR9vRcuWLNXZlNyDfIcPM8hHRL61dasWly5JgROVSsS99/pvqc66dO1qR79+8jl1ZqahlqMp0JhMwJkzcpCPmXxERESBrU0bJ1q0kJMeTpzg9TGFLo+DfN988w0efPBB/Pa3v8XPP//szT4RhazkZCPU6oZNkKvVIpKTA3dChYgClygCGzbI9czuuIMbXTe13r2rZvKJjKkSkQ+lpcmBsHHjzIiLC+zqEtOmyefUq1frYWOyV9A4eVLtKiur1Yro1IlBPiIiokAmCFWz+dwz9olCjcdBvgMHDgAAWrZsiS5dulzz+LPPPotnn30Wn3/+ucedIwo1cXFOJCVVTi7UNXMrPZ6UZERsbGBPqBBRYDp+XIWzZ+UT6XHjWKqzqbkH+fLzlbh8mUUZiMg3Ll9WYNMmeaFHSkrgLzqbNMkEjUY6x87PV2LbNq2Pe0SNlZenwMKF4XjyyRaun7Vo4UBREb8/iYiIAl337vKinWPHmMlHocvjM9uLFy8CADp16lTt4/n5+cjPz0d5ebmnT0EUkubNK0FCggVAXbWkBSQkWDB/fklzdIuI6Brr18uTu336WNGuHRccNLXoaCfatZMvZH78UePD3hBRKFu2zODKjOrQwY5hwyw+7lHjtWwpYuxYecFKRgZLdgYqkwl47rkoDBzYGqmpkVVKeF29qsLAga3x3HNRMHN9EhERUcByz+Q7fpyZfBS6PA7yGY3SSs3w8HCvdYaIAL0eWLy4ACkpFbWW7hw92oQlSwqg09V4CBFRk3Iv1Tl+PGfJmov7vnw//sjVikTU/BwOYOlSOQA2Y4YRiiBJjJKragCbNulQVFTXwjvyNyYTMGtWDNLSwmCzVf/vZ7MJSEsLw8yZMTCx2jgREVFA6tFDXgB7/Di3s6DQ5fGlmOKXqziHw+G1zhCRRK8HUlNLsHfvVcyZU4phw8zo39+K6Gj589aypcgAHxH5zMWLShw+LGeR3XEHg3zNxT3Id+gQg3xE1Py2bdPi0iVptbRSKWL69MAv1VlpxAgLYmOlc26rVcCaNXof94gaau7cKGRna1Gf7Q+ys7WYOzeqObpFREREXtatm3xtXFyswNWrQbLqjKiBPH7nt2rVCgBw+vRpOJ0sz0XUFGJjnZg9uxzp6YX44ot8vPaaXJpz3TodjEauLCYi33DP4uvc2Y4uXey1HE3e5L4vHzP5iMgXliyRs/jGjTOjdevguR5UqYC775ZTuzIzWbIzkOTmKtzKrNa9/QEglWXNy+OkIBERUaCJjhbRurWcEHH8OK+PKTR5XKy2d+/euHz5Mi5fvox//vOfGDJkSLWlO4uKinD8+PEG//7u3bt72jWioDV2rBmRkU6UlipgNCqwfr0OiYmsL0NEzc89yHfHHSYIXHPQbNyDfLm5Sly5okCbNsEzwU5E/u3yZQU2bZK/A2bODJ4svkrTphnx3/9K17Y//KDBiRMqdOvGxSyBID3dUGOJzprYbALS0w2YPbu8iXpFRERETaV7dxuuXlUCAI4dU2H48MDfJ5qooTwO8o0fPx47d+5ERUUFfvjhB/zwww/VHrd//37s37+/Qb9bEAR88sknnnaNKGjpdMDEiSakpYUBAFas0DPIR0TNrrBQgT175FKd3I+vebVq5UTbtg5cvixdyBw6pEabNryQIaLmsWyZAU6nFERp396OYcOCb/y58UY7evWyuspSZ2bq8Ze/lPm4V1Qfu3dr6j6oGtnZGsye7eXOEBERUZPr0cOOrCzpNjP5KFR5XJOiRYsWeOaZZxAdHe3N/gAARO6SSVQj96Dezp1aXL7M0jJE1Ly++UbrmuBt3dqBvn1tdbQgb7v5Zqvr9uHDvJAhoubhcABLl8rlK2fMMEIRpKeiSUnyOfeqVQZwK/rAUF7u2RvS03ZERETkWz16yPMRx497nM9EFNAa9c6//vrrsWDBAhw+fBiXL1+GxSKv4lyzZo3rmN69ezeul0TkMnCgFdddZ8f58yo4nQI+/1yPRx+t8HW3iCiErF+vd92+/XZz0E7w+rPevW34+mvp3+HQIc+yFoiIGmrbNi0uXZIuIZVKEffeG3ylOitNmWLC/PmRsNsFXLmixI4dWowYEXxZi8EmPNyz8tWetiMiIiLf6t5dLql+/LgKTic4R0Ehp9HhbZVKhb59+6Jv375Vfl4Z5OvcuTOmTJnS2Kchol8oFMA995jw7rsRAIAVKwx45JEK7odFRM2iokJAVpbWdf+OO1iq0xduvllerfjjj8zkI6LmkZYmZ/GNG2dG69bBGxiJiXFi9GgzNm6UFlRkZOgZ5AsA8fFWZGXp6j7wVxISrHUfRERERH7Hfd9kk0mB8+eV6NSJJRgotDCuTRSAEhPlVdPHjqlx5AjT0YmoeWzbpoXFIq0qiIx0Ij6eE56+0Lu3HOS7ckWJ3Fye0hFR07p8WYFNm+TgSUpK8GbxVXIv2blxox4lJVxV5++mTDFBEBq2/YdaLSI5Ofjfz0RERMHIYBDRsaN7Nh8XwVLo4YwQUQDq3NmB/v3l1aYrVxpqOZqIyHs2bJAneMeMMUPDSpE+ERfnROvW8urEQ4d4IUNETWvZMgMcDinI1b69HcOHB/8ij1GjzGjZUhprzWYBX3yhr6MF+ZLRKOCZZ1pAFOsbjJWCgUlJRsTGBm9WKhERUbDr3l1eBHvsGBMhKPQwyEcUoKZOlVebrl6th91ey8FERF5gs6FKFsf48SzV6Uss2UlEzcXhANLT5UVlM2YYQ2KvE41GKpNfKTOTC+v8ldEo4L77opGdra37YBcBCQkWzJ9f0mT9IiIioqb36335iEJNk12azZkzB3PmzMHYsWOb6imIQtrEiSao1dLq07w8JXbsaMgFLRFRw2Vna1FaKp06aLUi9ybyMfeSnQzyEVFT2r5di5wcacJEqRQxfXrolDacNk0O8n3/vQanTyt92BuqTkWFgFmzqgb47rnHiBkzKlzXS7+mVotISanAkiUF0DV8Cz8iIiLyIz16sFwnhbYmC2336NGjqX51o/33v//Ft99+izlz5qBnz551Hn/y5EmsX78eJ0+ehMlkQps2bTBs2DCMGTMGijqWsDocDmzZsgW7du3CpUuXoFQq0aVLF0yePBldunSp87nz8/Px5Zdf4scff0RJSQkiIyMxYMAATJ48GWFhYfV+zRR8oqNFjBljxvr1UtmgFSv0GDmSE+5E1HTWr5dnwYYNsyAsrGF73pB33XyzXLb50CHWTSWiprNkiZzBNm6cGW3ahE5pw169bOjRw4Zjx6QJo8xMA+bMKfNxr6hSRYWUwbd7txzgS0oy4s03i6FUAs89V4b0dAOyszUoL1cgPNyJhAQrkpNZopOIiChYuJfr/PlnFaxWcGsRCikhl7/69ddf49tvv6338Zs2bUJaWhpEUZ7IvHjxIpYuXYojR47giSeegEpV/Z/RarXirbfewvHjx6v8/Mcff8Thw4fx+9//HoMHD67xuX/++We89dZbMJnk1aOFhYX4+uuvceDAATz//POIiYmp92uh4JOYaHIF+TZs0KGsTEBEBCfdicj7nE7g66/lIN8dd5hqOZqag3sm3+XLSuTnK9CqFScsici7Ll9WVCnVnJISOll8ACAIwLRpRvz971EApIV1zz5bFhLlSv1dZQbfnj1ygG/6dCP+8Q8pwAcAsbFOzJ5djtmzfdRJIiIianI33GCHSiXCbhdgtws4c0ZVpYQnUbBrtksTi8WCAwcOIDMzEx999BE+/vhjZGZm4sCBA7BYmif7aMeOHUhPT6/38fv27XMF+HQ6He6++248+eSTmDx5MlQqFQ4ePIjly5fX2P799993Bfiuu+46/O53v8Pjjz+O3r17QxRFfPLJJzh9+nS1bXNzc/Huu+/CZDJBEAQMGzYMs2fPxqxZsxAdHY28vDz885//hJ0bsYW0UaPMaNFCmtA1mxVYt461ZoioafzwgxpXrkgzZgqFiDFjmDnsa61bOxEb63DdZ8lOImoKy5cb4HAIAID27e0YNiz0xv977jFBqZQW0l26pMKuXVwa7ms1BfgqM/iIiIgodGg0UqCv0rFjIZfXRCGuWd7xX3/9Nb744guUl5dX+3h4eDgmTJiA22+/vcn6sG7dOmRmZlbJyKuNxWLB4sWLIYoitFotnn/+eXTq1AkA0K9fP7Rv3x6LFi3Cpk2bMGzYMHTo0KFK+3379mH//v0ApNKlzzzzDNRqafLtlltuwXvvvYcDBw5g8eLFmDt37jXPn56e7vp73X///RgxYoTrsd69e+Nvf/sbzp49i23btmHMmDEN/XNQkNBqgUmTTPjsM6l064oVBkyfzuwaIvK+DRvkRQS33mpFTAwzxnxNEICbb7ZhyxZpNvPQITXLNhORVzkcwNKlcqnO5GRjSAZQ4uKcGD7cgi1bpO/CzEwDhg611tGKmkpFhYCZM6Px3XfXBviYYUlERBSaune3u/bjO3ZMjcmTzT7uEVHzadJTYKfTiX//+99VAlbVKS8vx7Jly/Cvf/0LTqd3Jw3NZjMWLVqEjIwMKBQKDB06tF7ttmzZguLiYgDA5MmTXQG+SgMHDsQNN9wAURSxadOma9qvXr0aAKBWq/HQQw+5AnwAIAgCpk2bBkEQcObMGZw8ebJK27Nnz+LAgQMApICee4APAGJjYzFq1CgAqPa5KbQkJsolk779VoucnBCceSGiJuce5Bs/nifL/sK9ZCcz+YjI27Zv1yInR1oXqlSKuPfe0CrV6S4pSX7t69bpUF4u+LA3oau8/NoA3733VjDAR0REFOLc9+U7fpyZfBRamvQ0OD09HXv37nXd79ixI+666y7MmjULs2bNwl133VUleLZv374GldOsjzNnzmDv3r0ICwvD008/jdtuu61e7Sr7rVarrwmyVaoMtFVm7FW6dOkSLl68CADo379/tfvm/eY3v0GPHj0ASK/b3Xfffee6PXbs2Fqf+8qVK8jJyanr5VAQu+UWGzp1klPSV63S+7A3RBSMTp5U4dQpOYB0++0M8vkL9yDfoUMM8hGRd6WlyVl8Y8ea0aZN6GZxjx1rRlSU9PpNJgW++opl8ptbTQG+f/yjhAE+IiKiENejhzw3WpnRRxQqmuxU+Ny5c64sM41Gg0cffRQvv/wypk6dilGjRmHUqFGYOnUq5s6di0cffRQajbSvwebNm3Hu3Dmv9qVv37549dVXcdNNN9XreLvdjvPnzwMAunXrBoPBUO1xXbt2BQCUlZUhNzfX9XP3ffb69OlT4/N06dLlmuPd72s0GvTs2bPatjExMYiOjgYAnDp1qtbXQ8FNEICpU+WVxStW6FHPqrRERPWyfr08kdmrlxUdOjhqOZqa0803y+XicnJUKCzkLCcReceVKwp88408/qekhG4WHwDodMDkyXJZ/MzM6q8RqWlUBvj27pUDfMnJDPARERGRxD2T79w5JYxGVl2g0NFkp8Nbtmxx3X7wwQcxaNCgGo8dNGgQHnjgAQCAKIrYvHmz1/rRpUsXPPXUU2jRokW92xQVFcFul6L/nTt3rvG42NhYaLXSRUZeXp7r5+63KwN51ancx8/9ePf7nTp1gkpVc3px+/btq21Poeeee+QJh59/VjObg4i8auNGlur0V23bOhETIwddWbKTiLxl2TIDHA5pcqR9ezuGD+een9OmyYHO7Gwtzp9nmfzmUF2Ab8aMCqSmMsBHREREkuuuc0Cnk6ouiKKAkydZspNCR5OdEh89ehSAFIiKj4+v8/iEhAS0a9cOAHDs2DGv9cN9L7z6Kisrc91u06ZNjccJgoDw8HAAQH5+/jXtlUolWrVqVWP7iIgIAEBxcbErqOjevnXr1rX2s7K9+3NTaOrY0YFBg+SJlxUrWLKTiLzj0iUFfvhB47rPIJ9/EQSW7CQi73M4gPR0OVMtOdkIJeNZ6NfPhhtukMdcnnM3vbIyASkpMdcE+BYsYICPiIiIZEol0K2bPL9+7BiDfBQ6muzdXlxcDAC4/vrr692mc+fOyMnJcbX1FZtNvnCrDKTVJCwsDAUFBSgqKrqmfVhYGBS1XHmEhYUBkLIXi4uLXQHByvb1eW4AVZ67Ni+++OI1P9NoNEhNTQWAWgOSJKnMrIyNjfVxT6714IMCKrdzXLs2DP/8pxYexLiJKEh5On5lZsrfYzfcIGLo0JYQWPXCr9x6qwJbt0q3jx8PR2wsJ50puPjz+Vew2rhRwMWL0t9dqRTx2GN6ji2/ePBBBf76V+n2qlURePVVPYNNTaS0FJg6VYnvv5f/wL/7nROLFmmgUATOeMAxjIgCFccvCjR9+ihx6JB0+8KFSMTGhvu2Q+QzoTZ+NdnlSGUGndlc/xX/lcd6kn3XVOrqS+UbxmK5tnxNfds2tn11bSn0JCaK0Gqlzfjy8wVs3MhZeCJqvLVr5bFk8mQnA3x+qH9/eSPWAwf4D0REnrl6FXj9dQXuvFOJ++6T0/ZGjxbxm9/4sGN+ZsYMJwRBGnfPnBGwaxfH3aZQWgpMmKBEdrY8ZfH73zuwaJGDQVUiIiKqVq9e8rXxkSM8R6PQ0WSZfG3btsWpU6dw5MgRmEwm6PW1r/w0Go04fPiwq60vKd1q0Wg0mlqOlANtVqv1mvb1bQtUzR5UKpWw2+11tq98Hve2tXnttddqfTw/Px+iKNZ6TKirjP776z6IY8e2xJdfSp+1Tz6x4dZb65flSUTBz5Pxq6hIwPbtctnqYcOKkJdnraUF+ULHjkoAUonvs2cFDBggokULJxISrEhONiI21unbDhI1kr+ffwU6kwmYOzcKGRkG2GzXToZs3izgwQetmD+/BDpdNb8gxGi1wLBh0di+XfpjfPihBT16lPi4V8GlskTnvn1yNC8lpQJz55agoMCHHfMQxzAiClQcvyjQtG+vBRADADh0SOR7N4QF0vglCEKj42FNtgauX79+AKTg3f/93//Vefz//d//wWQyAQD69+/fVN2qF53b1av7XnnVqXzcPdBW2b6+bYGqQcL6tnc4HNe0pdCWmGh03f7mGx1KSrhqhYg8t2mTDg6HNI7Exjpwyy38vvE3JhOwcGE4AHmRzqFDGmRl6bBgQSQGDmyN556LQgMKKxBRCDGZgFmzYpCWFlZtgA8AHA4BaWlhmDkzBr9croW8adPkP8SXX+phNPKc21vKygTMmBGDffvkBa8zZ1bgjTe4Bx8RERHVrnt3eX7+yhUliop4jkahoclOk0ePHo3IyEgAwJ49e/Dmm2/izJkz1xx3+vRpvPnmm/jul83EIiIiMGrUqKbqVr2Eh8v1evPz82s9tqKiAgCq7L1X2b6wsBBOZ82r5yvb1tS+rucuLy+/pi2FtpEjLYiOloK/Fovgyuojqk5engILF4YjOTkaEye2QnJyNN57Lxx5eRxTSLJxo7zoZdw4MyfX/Ezl5PzSpWEAqr94sdk4OU9ENZs7NwrZ2Vq4LxSonojsbC3mzo1qjm75vfHjzQgPl67zyssVWL+eKY7eUFoqBfj275cDfLNmVeD11xngIyIiorq1aeNEVJQ8F3/ihP9sCUbUlJqsXKdOp8MjjzyCN998E06nE0eOHMGRI0dgMBjQsmVLAEBRURGMRjnzSKlU4pFHHqmSSecLkZGRUKvVsNlstQbanE4nSkqk0izufY6JkdKCHQ4HioqKXPd/rbi42HX71+2vXLlSZ5Cvuuem0KZWA1OmmPDJJ1KgeMUKPVJSjHW0olBTW1murCwd3n47AklJRpblCnEmk4CtW7Wu++PHMxXM31SdnK9thaI8OZ+aypJyRCTJzVUgI8Pwy726VjlLj2dkGPDss2UhXwZYrxcxaZLpl0UWQGamAYmJXEnRGJUBvgMHqgb4XnuNAT4iIiKqH0GQsvm++06ayzh2TIVbb2VFIgp+TXq63LNnTzz55JNV9uMzGo3IyclBTk5OlQCfTqfDE088gRtvvLEpu1QvCoUC1113HQDg3LlzNR6Xk5MD8y/1r9wDeZ06dXLdrq39zz//7LodHR19TfsLFy7Umgl46tSpa56baOpUeYLhu++0OHdOWcvRFGrqU5YrmDN/mL1Yf9u3a2E2S3+X8HAnBg+2+LhH5M7TyXm+14moUnp69Xvw1cZmE5Cebqj7wBDgXrJz504NcnI4vnqqugDfffcxwEdEREQN1727vP3V8ePM5KPQ0OSnzL1798brr7+OsWPHokWLFtc83qJFC4wbNw6vv/46+vTp09TdqbfKYONPP/3kCuT92vHjx12327dv77rdtm1bV7bi/v37a3yOyvYxMTEwGOSL5crnLi8vx4kTJ6pte/HiRVe5TvfnJurd24YuXeQa1KtWsWQnyUK1LJfJBDz3XBQGDmyN1NRIZGXpsH8/9y2rzYYNchrn6NFmaLW1HEzNjpPzRNRYu3dr6j6oGtnZnrULNgMHWtGpkzSJJIoCVq7k+FqbmhZanT6tZICPiIiIvKZHD3lO9PjxJitiSORXmuWdHhUVhRkzZmDGjBkoKChwlZmMiory2yy0QYMG4YsvvoDZbMbmzZtx1113VXnc6XRi+/btAACNRoPOnTtf037jxo3Ys2cPpkyZglatWlV5/OzZszh79iwAKePRXffu3REZGYnS0lKsW7cOPXr0uKZ/W7dudd3+dXsKbYIgZfO98Ya0WmXFCgOeeqocAveaDXn+VJYrL0+BpUsN2L1bg/JyBcLDnUhIsCI52ej156rMXpSCm9WrzF48fVqFxYsLoA/x2LjdDnzzjRzku/12Rj/9TWMm52fP9nJnfKQ5xxGiYFRe7lkExdN2wUY65zbizTelfegzMw144gmec/9aXWXiU1MjIIryzysDfPw7EhERkSfcM/mOHVNDFMHzCgp6zX6FFhMTg86dO6Nz585+G+ADgA4dOqBr164AgDVr1uDkyZNVHl+zZg0uXLgAABg4cCC0v0pxGDlyJARBgM1mwwcffACLRS5zVlFRgY8//th1f/DgwVXaqlQqDB8+HABw6NAhbNy4scrjhw8fxrZt2wAAsbGx6N69eyNeKQWje+6RywedPavCvn1MTyf/yPzxRUZdqGYvNsbu3RoUF0unCBqNiFGjWKrT34Ty5Dwzc4m8Izzcs2C4p+2CkXuZ/NOnec79a/UpE+8e4EtJYYCPiIiIGqd7dzmTr7hYgdzcwL8GJqoLc1ZrMX36dLz++uuwWq1YsGABhg0bhjZt2uDgwYM4cuQIACmLb/Lkyde0bdOmDUaNGoXNmzfj5MmT+Otf/4qhQ4dCFEVs374dhYWFAIBevXpVm4k3fvx47Ny5E0VFRUhPT8fRo0dx00034cKFC9i1axccDgcAIDExEQKvguhX2rVz4LbbLPj2Wyn4vHKlAQMGlPi4V+Rrnmb+/Otf4Th4UI0OHRy//GdHhw4OXHedA2FhdQXOZL7IqPOn7MVAsnGjnMU3ZIgFERH1/3em5hGqk/PMzCXynvh4K7KydHUf+CsJCdYm6E1g6tCh6jl3ZibPud1VXWhVv2tWXtoSERFRY0RHi2jd2oGrV5UApH35WrfmwmUKbgxl16JLly546KGHoFKpYLfbsWXLFixdutQV4FMqlXjooYcQFxdXbfvk5GQMGDAAAJCXl4dVq1Zh9erVrgBfmzZt8PDDD1fbNiwsDE8//bRrb78ffvgBaWlpyMrKcgX4xo0bh/j4eK++ZgoeU6caXbfXrtXDwu+zkOdpBk9FhQIbNujx4Yfh+NvfovDggzEYMyYO3bq1Ra9erXHnna3w8MMt8corkfj0UwO2bNHi5EkVTKaqv6c5M+ocDqCkRMAHH4T7PHsx0Ihi1f34xo9nOpQ/io/3bJI90CfnmZlL5D3JyUao1Q1bxKFWi0hONtZ9YAiZNq3qOfevz39CVcMWWkkyMgzIy+MUBRERETWOezbf0aPMcaLgJ1RUVHB5fh0uXryIzMxMHDp0CKIoQhAEdO3aFUlJSejSpUutbUVRxM6dO/Hll1/i6tWrAACtVovBgwcjMTERYWFhtbYvLy/HqlWrsGvXLlfJz7i4OEyePPmaMp+eMhiki6/Lly9DFPl2qE1sbCwAKWjr78rKBPTt2xpms3Sh/NFHhbjjDk7Wh7I772yFgwc9y+bzVGyslP0XG2vHN9/o4XTWP+CmUol4//1CKJVSgLKsTHD7v4CyMgXKy6WfSfflx43Gxk0QDRtmRnp6YaN+h7+p7/h18KAad94pHSsIIg4cuBrSWY3+KjdXgUGDWjcoiK1Wi9i7N3D/PUPxNZMkkM6/As1zz0UhLS0MdWdaSY+npFQgNZWZau4qKqRz7spzj3//uxCTJ/Oce+HCcKSmRja43Zw5pZg9u7wJeuQ7HMOIKFBx/KJA9fLLkfjww3AAwPTpRrz9drFvO0TNLpDGL0EQ0LZtWwCA0ejZgkqGsuuhffv2+NOf/gSj0YiCggJERUUhMrJ+FyyCIGDo0KEYOnQo8vLyYLVaERsbC42mfhPt4eHhuO+++3DvvfciLy8PGo3G9SYlqk1EhIjx4834/HMpiLtihZ5BvhB1+LAKb7wR6XGAb9IkI266yY4LF5S//KfCxYtKWK11T7Tn5SmRl6cE0PDnttsF/P73vtm7NRj2LfOUexbfwIFWBkf8VFycE0lJxgZNziclGQP637Mx+4oG24QxkbfMm1eC/fs1OHq0rr3kBCQkWDB/PgN8vxYWJuKuu8zIzKw85zYwyAfPy8RnZ2swe7aXO0NEREQhpUcPOZPv+HGGPyj48V3eAAaDwZX15onGBOc0Gg3atWvncXsKTVOnmlxBvs2bdSgsFBAdzWzNUPHzz0r84x+R+PJLzzekUqtFzJ9fek1gwOmUsmouXFDhwgUlzp9X4uJFJc6flwKAOTlK2O2Bu6lKoO9b1hjuQb7bb+ckpT+bN68Ep0+rat2fThIck/OcMCbyPr0e6NbNVmuQT60WkZRkxPz5JdA1fAu/kDBtmtEV5Nu2TYsrVxRo0yZ0zyUAzxdMhfJCKyIiIvKO7t3trtvHj6vgdAIKnmJQEGOQjyiIDR1qQWysA3l5SthsAtau1eOBB7iPSrDLyVHgnXcikJFhgMNRNdDWqpUD+flKNDbzR6EA2rRxok0bKwYOvLa13Q5cvSoF/y5cUCI1NRKXLysb/FrUahGdO9sRHi4iIsL5q/+LCA93IiJCRFiY9H/3n4WHO/G//4XhzTcbXioq0Pct89SpU0qcOCFP9DL717/p9cDixQWYOzcKGRk1ZbmJSEkJjsl5ThgTeV95uYCvv5YHhylTjCgsVKC8XIHwcCcSEqxITg7sLODmkJBgRfv2dly8qILTKWD1aj0efbTC193yKU8XTIXyQisiIiLyjm7d5CCfyaTAhQtKdOzo8GGPiJoWg3xEQUylAu6+24T//leqQ71ypYFBviBWUKDAe++F47PPwq4ppdmzpw3PP1+K226z4L77Ypo880elAtq1c6BdOwcSEoDVq/UeBfkSEiyN2hsvJcWIhQsjGljiT0T79va6DwtCGzfKWZ89e9p4EhwA9HogNbUEzz5bhvR0A7KzNSgsVODwYTWkQL6AadNMAR/gAzhhTNQU1q3TwWSSAuFRUU68/XYxtHWdItA1FAqpgsa770YAADIzDXjkkQoIgVvUoNHi463Iymr4l0+oLrQiIiIi7wkLE3HddXacPy+FPo4fV3F+g4IalzYTBbnERDmot3+/BqdONTzQQv6ttFTAP/4RgYSEOHz0UXiVAF+nTnYsWlSEr7/Ow5gxFhgMUuZPSkoF1OrqS7eq1SJSUiqwZEmB1wID8fGeTdg0dqKnct8ySX1L1Qp44olovPlmBBwhdg64fr38Dz5+PLP4AklsrBOzZ5cjPb0QGzfmY8gQ+bOTkeF5yV5/4qtxhCiYrVghb0UwaZKJAb5GmDpVPuc+flyNQ4fq2ucwuCUnG2s816yJWi0iOZkLEomIiKjx3Et2HjsW2udlFPwY5CMKcjfdZEfPnvKGs6tWeb6vJPkXkwl4//0wJCS0xrvvRqCiQh7S27RxYMGCYmzblospU0xVao9XZv7s3XsVc+aUYtgwM/r3t2LYMDPmzCnF3r1XkZrq3dJ+vpzomTevBAkJFtRenlSiUMh9fOedCNx/fzQKC0NjGf6VKwrs3y/veTZ+vMmHvaHGkoPbwNq1ehiNgf8+5oQxkXfl5Cjx7bfyuO8epKKGu/56BwYNsrjuZ2YGxwILT1VdaFUXaWyvqUw8ERERUUP16CHPhR4/zmKGFNwY5CMKcoJQNZtv5Uo9nLx2Dmg2G7B4sQFDhrTG3/8eheJieShv2dKBl14qwc6dVzFzphHqWhYruWf+fPFFPtLTCzF7dnmTTK40LKPOuxM9lfuW1Sd7cd26PHTuLK/22rpVhzvuiA2J1fjuezJ16GDHTTeFZsnSYHHnnWZEREifn/JyBdatC/x6nb4cR4iC0cqVeoiitACgUyc7brnFVkcLqsu0afICmdWrDbBYajk4BDz+eFmVBVQ1a1yZeCIiIqJf69GDmXwUOhjkIwoBd99tcl1gX7igwt69mjpaUHPKy1Ng4cJwJCdHY+LEVkhOjsZ774UjL6/qEO10SnvbjRgRh+efb4ErV+TSq2FhTjz9dBmys3PxyCMV0Pvh4vH6Z9R5f6KnvtmLN99sx7p1ebjjDnmS7uJFFaZMaYVly/zwj+pFGzbIQaDbbzeH9D5CwUCvFzFpkvw+zsgIjizuefNKcMMNNtRnHOnTx8oJY6IaiCKwYoX8vTZ1qpHjvhdMmGCCTiedcxcXK7B5c+AvsGiMf/0rAk5n5Rur+crEExEREXXvLi9g+/lnFazcxYGCGHNViUJAmzZODBliQVaWdOW8YoUet97KbzdfM5mAuXOjkJFhgM1WdWYtK0uHt9+OQFKSEfPmlWDnTi0WLIjE0aNVVx9ptSLuu68CTzxRjpgY/85Wqcyoq+k1A9JET1KSEfPne7dcaKXK7MXZs2s+JiJCxIcfFuH996147bVIOJ0CLBYBzzzTEvv2afD3vzdN33yppETArl3yRkx33MH9+ILBtGlGpKWFAQB27dLiwgUlOnQI7I0m9XogPLx+JTsFAdBwTQtRtX74QY1Tp+RzisRElmj2hshIEXfcYcLq1dLCisxMPe68MzS/U0+cUCE9XV5gMmdOGQAgO1uD8nIFwsOdSEiwIjmZGddERETkfTfcYIdKJcJuF2C3CzhzRlVlnz6iYMIgH1GImDrV5AryffmlHvPnl/hltleoMJmAWbNikJ2trfEYm01AWloY1qzRo7y8alafUili+nQjnnqqDO3aBc7ESGVG3bPPliE93eC3Ez2CADz6aAVuvtmGP/6xJQoKpKzJpUvDcPiwGh9+WIT27QM7WOJuyxYd7HYp6Bod7cDAgVwEEAwGDLDhhhtsron8zEw9nn663Me9apzDh1U4eFCO3D34YDlOnVK5xpHWrZ3IzJQmlX/4QYPPPjPggQe4zxjRr61YIQdf4uMtuO664PlO87Vp0+Qg35YtOuTnK9Cqle/PbZrbq69GurL4OnWy45FHyqHRoNaFVkRERETeotEAnTvbceKEdD187BiDfBS8WK6TKETccYcZBoM0wVBaqsA33wRZKlKAmTs36pcAX90ZKb8O8E2aZMLWrbn4xz9KAirA56459wNsjCFDrNiwIQ/9+8tBr0OHNLj99lhs21ZzgDbQrF8vjwfjxpmhVNZyMAUMQQCSkuTsnMxMQ8Dvybp4cZjrdv/+VrzySmmVceTdd4sxZYoc1Hv99UhcusTTXSJ3Vivw+efySq9p0xgI96YhQyxo00YKmtrtAlavDr1Vdbt2abBpk3xu8eKLpcysJiIiombnHtQ7fpz78lHw4qwHUYgwGMQq5YJWrgyO/ZkCUW6uwm1/rPpvgDNkiBkbN+biP/8pwg03cMV9c/nNb5xYuTIfDzxQ4fpZcbECM2dG4513wgM+aGIyAVu3ygHL8eNDs6xYsEpMNLr2ZD1/XoXduwN3lrW8vOpk+axZFdUe9/LLpWjRwvlLGwVeeimqWfpHFCi2bNGhuFi6DNTpRNx1F8d9b1IqpT0OKwXLnqj15XQCf/97pOv+gAHWkC1ZSkRERL7lvi/f8eMsaEjBi0E+ohCSmChPOGzdqkV+PocAX0hPr34/uroMHmxFr14sLeALGg3w6qsleO+9Iuh0UvBAFAW8+WYkHnggGsXFDf/39Bc7d2phNEpjgcHgxNChFh/3iLypbVsnhg+X/02XLw/cyeZVq/SoqJDeq1FRTkycWP0eYrGxTrz0Uonr/oYN+irZqkShbsUKOVg+frwJERH12+eS6m/aNHl8+uknNY4cCZ1Jpc8/1+PHH+UFJS+9VAIhcE+TiIiIKID16CHPoR07xkw+Cl6c4ScKIYMHW13lgxwOAWvWhF75IH/gaSZNdnbgZuAEi8REE774Ih+dOsknips363DnnbEBO4G3YYMc/Bg50gIdYyFBJylJXuDx1Vc6lJcH3myrKFYt1TltmrHWfWWnTzchIUEObv7lL1EoLQ28103kbYWFQpUyilOnVh8sp8bp0sWOfv3kUt+Ve4UGO7MZeOONCNf9u+4yYcAAWy0tiIiIiJpOjx7yeci5c0qYTLwmpODEIB9RCFEqgXvukSd73VdyU/P59R57Td2OvOvGG+1Yty4PY8fKpafOnVNh0qRYZGYG1mfKbge+/lqe7L3jDpbTCkbjxpld5StNJgW+/DLwIrn796vx00/yysuZM2vfQ0wQgAULiqHVShlKV68q8frrkbW2IQoFa9fqXdUE4uIczN5uQu57Ha5apYctBGJd//tfGHJypEVParWIF14o9XGPiIiIKJRdd52jSjWmEycCc3E2UV28PmP88ccf4+OPP8Y333xT5zHZ2dnefnoiqkNiorxi+9AhDb/gfCA83LNN3DxtR94XFSXik08KMWdOKQRBCiKYzQKeeqolXnghCpYAmTP9/nsNCguVAKTJuNGjGeQLRjodMHmyPPYHYsnOJUvkLL6EBAu6dq27dPENNzjw5JNlrvuffRaGvXuZEU2hbcUK+fN/990mqHga2GQmTTJBo5HOEQoKlFX2vw1GhYUC3ntPzuK7774KXH8995AmIiIi31EqgW7d3Et28uSXgpPXg3w7d+7Ezp07ceTIkTqPOXXqlLefnojq0KOHHb16yeWDVq4MrMyjYBAfb637oGokJHjWjpqGQgHMnl2OpUsL0bKlPIn12WdhSExshZwc/8+8dN+n7LbbLIiM5L5MwWr6dDmj5LvvtDh9WunD3jRMcbGAtWvl76pZsyrq3fbRR8urlGh57rkoWDmUUoj6+WcVDhyQA91Tp9aeEUuN07KliHHj5MUzwV6y8913I1BaKp37REY68dRT5T7uERERERHQvbsc5Dt+nPvyUXDyeAbyk08+wZdffonvvvsO586dg8nE/RyIAoX7/isrVxrgZIJYs0pONkKpbFgwRa0WkZzMyTh/NGyYBRs35qNPHzlycOCABuPHx2LHDnkyNS9PgYULw5GcHI2JE1shOTka770Xjrw83wQDRbHqfnzjxzOLL5j17m2rEuwKpMnmFSsMMJul8oIxMY4GvVc1GqlsZ2XG7YkTaixaFN4k/STyd+5l2m+80YYbb6w7I5Yax71k5zff6FBYGJz7wJw9q8Rnn8kZ1088UY7oaF5gEBERke+5XwcfP85MPgpOHr+zd+zYcc3PwsPlSZOcnBysXbsWcXFxiI2NRWxsLCIjuRcKkT+YMsWEv/89Eg6HgMuXlfj2Ww2GDGFqQ3M5ckTdgMCqCEBAUpIRsbGcLPFX7do5sGpVPv72tyikpUmTXIWFSsyYEYOnny7DpUtKZGYaXPsgVcrK0uHttyOQlGTE/Pkl0DXjVmlHjqhw8aJ8GnD77QzyBTNBkCab//73KABSkO/Pfy6D0s8T+kQRWLxYDkjee68R2gZWvBswwIb77zfi00+lz+Z770Vg4kQTunRhGTkKHU5n1eoNzOJrHiNGWBAb60BenhI2m5SV/MADwfe3f/31SNc5Trt2dvz2t8ziIyIiIv/gnsl37Bgz+Sg4eRzkGzlyJHJzc5Gbm4uCggI4nU6Ul8sn8/n5+Vi9enWVNlqtFq1atXLdP3/+PL799ltXELBFixaedoeIGiA21onhwy3YskWKKKxcaWCQr5kcOKDGQw+1hCjWdyW3gIQEC+bPL2nSflHj6XRAamoJbrnFihdfbAGzWYDTKeDNN2tf4GKzCUhLC8Pp0yosXlwAfTNV0N2wQX6i/v2taN2aQeRgl5howmuvyQs8du7UYvhw/95AcvduDX7+Wb4QmzHDs8nx558vxYYNOly5ooTVKmDOnBbIzCyAwv+r6hJ5RXa2BpcuSZd+SqWIu+9mFZbmoFIB99xjwgcfSIthMzIMQRfk+/57Nb78Uj6nmDOnrFkXLRERERHVpnt3OZPvyhUliosFtGjBrUoouHgc5Lvvvvtct51OJ/Lz85Gbm4u33noLANCyZUu0bdsWeXl5riCgxWJBTk6Oq93Jkydx8uRJ1321Wo2YmBjExsbi6aef9rRrRFQPU6caXUG+r77S4bXXBOj1/JJrSj//rMR990XDZJJmlQ0GKdi6aZPumgwvQCrR6YsML2qc6dNNuOkmGx56KBrnz9f3a1ZEdrYWc+dGITW1eQK67qU677iDWXyhIDbWidGjzfj6a2kydvlyvd8H+ZYskbP4hg83o1Mnz7LvIiJEvPpqCX73u2gAwO7dWixbZvA4aEgUaFascP8sWRAXx4UdzWXaNKMryHfwoAYnTqjQrVtwlEoVRbgyxAHg5putDCATERGRX2nb1onISKdr7+ATJ9QYNIiJDhRcvFKIVqFQIC4uDnFxca6fXXfddXjqqacASEHAwsJC5OXlITc3F59++ikAKRCo0+mQn58Pm80Gm82GK1eu4MqVK97oFhHVYtw4MyIinCgrU6CiQoENG3S8KG9CV64okJISg8JCqTaeWi3i44+LMGyYBXl5CqSnG5CdrUF5uQLh4U4kJFiRnMwSnYGqVy87PvusACNHxtUza1M6JiPDgGefLWvyf/ezZ5U4elTOjrr9dn72Q0VSkskV5NuwQY+SkhJERfnnAo/8fAW++krODpk1q3EBufHjzbjzThPWrZN+5yuvRGLMGDODHRT0jEYBX30lL+xgqc7m1bOnHTffbMWPP0r79GZm6vGXv5T5uFfesX69Dt9/L+8//Ne/ljJDmoiIiPyKIEjZfHv3Svs+HDumYpCPgk6z7DapUCjQqlUrtGrVCj179nQF+fr374+ZM2cCAEpKSpCXl4f8/Hzk5eU1R7eIQppeD9x1lwnLlkl7FK1YoWeQr4mUlAiYOTOmyv5nCxdKAT5Ayq6ZPbscs2f7qofUFNat0zegLKvEZhOQnm7A7NlNu5eNexZft2423HAD9yYLFaNHmxEd7UBhoRIWi4A1a/S47z7/nPDPyJD3sWzTxoExYxqfcTp/fgl27NCirEyBkhIF5s6Nwn/+U9To30vkz9av16GiQoq8REQ4MW4cs7eb27RpJleQb+VKA+bMKYOqWa7Em47NBrz2mlySfPRoM8v/ExERkV/q0cPuFuTjvnwUfPxmnV1UVBS6dOmC+Ph4TJw40dfdIQoJU6fKQb2sLC2uXvWbISFomM3Ab38bXSVrat68EkyezAm2YLd7t6bug6qRne1Zu4ZwD/KNH8/3YijRaKT9oSplZBhqOdp3nM6qpTqTk41Qe+FarG1bJ154odR1f+1aPTZt0jb+FxP5sRUr5IzYiRNNzbb3K8nuvtsEtVrKmr56VYkdOwJ/3FmyxIAzZ6RIpUIh4q9/La2jBREREZFv9Ogh78t3/HiAr7QiqobHM/ovvPAC3nnnHaSlpeGbb77BwYMHcfnyZW/2jYia2K23WtGunbQniNMpYPVqzvp4k8MBPPFES+zeLU/kPPZYGX7/+wof9oqaS3m5Z1+xZWVNG2zPy1NUKa3FIF/omT5dztw7cEDaH8rf7Nihxblz8uRxcrL3xs1Zs4wYMEDONnnxxShUVDQs65YoUFy+rKgSUHJf4EXNJzpa2hO1kr8usKiv0lIBb78d4bqfnGwMmn0GiYiIKPh07y6fpxw7pobonztWEHnM41mduvbOO3HiBN566y3ExsZW+c993z4i8i2FAkhMNOG996SL9JUrDXjkEQagvEEUgb/8Jcq19xMATJtmxAsvBMceLFS38HDP9vk6ckSN116LQFKSEV26eL+M5tdf61xlRH/zGzt697bV0YKCzY032tGrlxWHD0vB3owMg99lYLhn8Y0ebUG7dt7bN0+hAFJTi3H77bGw2QTk5KiQmhqBefP8629A5A2rVxtcY/5119kxcCDLKfrKtGkmbNggnRdu3KhDSYngt3ui1mXRonDXPtMGgxPPPMPzWyIiIvJf7kG+4mIFcnMVaN2ae7NT8PA4yPeHP/wBubm5uHr1KvLy8pCbm4uSkhLX4yaTCYcPH671dxw6dAj/93//h9jYWLRq1coVCAwPD/e0W0TUQImJRleQ76ef1PjpJxVuvJErcRvr3XfDsXhxmOv+qFFm/OMfxRCYLBIy4uOtyMrS1X3gr1itAhYtisCiRREYMMCKe+81YuJEE8LDvTMR+OtSnXxPhqbp002uIN/KlXo8/3yp3+wPdeWKAhs3yu/TWbO8v/ike3c7HnusHO++K33/ffJJGO6+24S+fRn0puAhikBmprzYKDHRBAUrs/vMqFFV90T94gs9Zs70zz1Ra5OTo8BHH8nX648+Ws5JMiIiIvJr0dFOxMU5kJsrLVI6flyN1q0tPu4Vkfd4PJ0THx9/zc8sFgseeeQRAED79u0xYMAA5ObmIj8/H3l5eSgqKqpyfF5eHrZt23bN79HpdPjPf/7jadeIqAG6dHGgXz8rDhyonOw14MYbmc3QGEuWGPDmm5Gu+/36WfHBB0Ve2U+KAkdyshHvvBMBm60hUTQRgHz8999r8P33Grz0UiQmTDBj+nQj4uOtHgfmSkuBnTvlsm23385SnaFqyhQj5s+PhM0mIDdXiW3btBgzxj8uctLTDXA4pDd5+/Z2jBjRNP164okyfPGFDqdOqeF0Cnj22RZYty6PYzUFjcOH1ThxQn5DT50aeAGlYKLRSHvzffyxFCDLyDAEZJAvNTUSZrM0RsfFOfCHP7AKCBEREfm/7t3triDfsWMqDBvmH9e/RN7g1TXbWq08cRgTE4PJkydXedxutyMvLw8vvvgiAKBTp07o2LEj8vLykJ+fj4KCAjgcDpjNnHQkak6JiUZXkG/1aj1efLEUSqWPOxWgNmzQ4YUXolz3u3Sx4bPPCmAwBGY5JvJcXJwTSUlGpKWF4dfBu2tJj0+dasLNN9uwbJkBR4/KE7MmkwKZmQZkZhrQqZMd06YZMW2asV4lDPPyFFi61ID9+5U4e1aA1Sr1IzLSifh4lm0LVdHRIsaONbtKCi9fbvCLIJ/djl8+M5KUFGOTfR/pdMCCBSWYOrUVACmb/cMPw/HHP5Y3zRMSNTP3LL6BAy3o1Mn7JaCpYaZNk4N8+/ZpMHlyDJxOAeHhTiQkWJGcbERsrP9mxR0+rMLKlfL76s9/LkNYGM9xiYiIyP916GAHIMUu/vWvcGzdqg2I8y+i+hAqKiq8elb+4IMPAgD69OmDp556qtZjRo8ejZkzZ7p+LooiCgsLkZ+fj+7du3uzW1QLg0Ha9+by5csQufNorWJjYwFIWajBpLBQgX79WsNulyb/ly4twPDhvp/sDTS7d2swY0YMLBbp79imjQNr1+ajXTtOqoUqkwmYNSsG2dnaOo9NSLBgyZIC6HRSibXDh9VYtsyAzz/Xo7j42vpqgiBi2DALpk834vbbzdD9qjKoyQTMnRuFjAxDtdmEgiBixgwj5s8vuaYthYZNm7S4//4YAIBaLWL//quIjvbtxc3XX2vx4INSn1QqEXv3XkVcXNP26dlno7B0qRRY1Omc2Lw5j8EQPxOs519NyWYDbrmlNQoKpCj5ggXFAZk1FmyMRmDgwDbVfq8D0liclOSf382iCCQnx2DHDumcpls3G775Js9vSj37M45hRBSoOH5RMKicG1m2TK4Y486fz7/Ic4E0fgmCgLZt2wIAjEbPrtn8alcGQRAQExPDAB9RM4uOdmLUKDmDdsUKfS1HU3WOHlXhwQejXQG+qCgn0tIKGOALcXo9sHhxAVJSKqBWV7+IQq0WkZJS4QrwAYAgADffbMOrr5Zg374r+Pe/CzFihBmCIP8OURSwfbsOf/xjNG65pQ3+8pcoHDqkhijKwcW0tLAay4WKooC0tDDMnBkDk8nrL50CwIgRFsTFSWOUzSZg9Wrfj/1LlshZfLffbm7yAB8A/OUvpYiNlf4OZrMCL7wQBa55okC3bZvWFeDTakVMmMCB3tdMJuC++2JqDPAB0ljsr9/N27ZpXQE+QBo7GeAjIiIif+Y+N1JdgA/w7/MvovryeibfsWPHAADh4eFo3759rce0bNkSrVu39ubTkweYyVd/gbQKoKG++kqHhx+OBgDo9U788MNVhIfz/VAfFy8qMXlyK1y5Ik+mpacX4NZbWQqRZHl5CqSnG5CdrUF5ucKj0lw5OQqsWGFARoYBZ89WP7PWs6cNer2I/fs1qG+Z0JSUCqSmljT0JVEQeOWVSPznP1LpuF69rNi4Md9nfbl4UYn4+DiIovSeXbYsH0OHNs84unatDo8+Gu26v3BhEaZO5RWevwjm86+m8vDDLfHVV1LgfsIEEz74oKiOFtTUnnsuqkElvP3pu9nhAMaNi8WxY1Ip8dtusyAjo8DjPYJDDccwIgpUHL8o0AXy+Rc1TiCNX97I5PN6kI8CD4N89RdIA0RDWSxAv35tUFIirS5+550iJCVxgrMuhYUKTJkSg1OnpEkPhULEhx8WYfx47i1KTUcUgT17NFi+3IAvvtDBZGpcYr5aLZVFZB360HPypAojRsS57n/9dS5uusnuk74sWBCB996LAABcf70dWVm5UDRTzQlRBO6/PxqbN0vptC1bOpCVlefz8qUkCebzr6ZQXCygX782rj1YP/20AGPHsgy7L+XmKjBoUOsas+ur40/fzenpBvz5zy1c9zdsyMPNN9t816EAwzGMiAIVxy8KZIF+/kWNE0jjlzeCfCywUQeHw4E9e/Zg7969OHPmDMrLyxEVFYUbb7wR48aNQ4cOHWpsW1xcjK1bt+LHH3/E5cuX4XQ60bZtWwwYMACjR4+GXl97Wazjx49jx44dOHHiBAoLC6HX69GlSxcMGzYM/fr18/ZLpRCn1QITJ5pcpdJWrjQwyFcHo1HAffdFuwJ8APDGGyUM8FGTEwQgPt6K+Hgr/v53AV9+qcOyZQbs3Vv33n/VsdkEpKcbMHt2uZd7Sv6ua1c7+vWz4sABDQBg+XID5s8vbfZ+2GzSJHKlmTMrmi3AB0ifqddeK8HIkRoYjQoUFSkxb14kFi4sbr5OEHnJF1/oXQG+mBgHRoxggM/X0tOr3x+3Nv7y3Ww0CvjHPyJc9++5x8gAHxEREfm9QD7/Imoov9qTz9/k5ubib3/7G/773//iwIEDKC4uht1uR0FBAXbs2IG//e1vWLVqVbXZb7t27cKzzz6LNWvW4PTp0zCZTLBYLDh79ixWrFiB559/HidPnqz2eS0WC/71r3/h9ddfx86dO5Gbmwu73Y6ysjIcOHAACxcuxDvvvAMTCwWTl7mXJtu1S4NLlzhE1MRmA/7wh5auiXEA+POfS5GS4tmKCyJPhYeLuPdeEz7/vABZWVfRoYNnWVjZ2Zq6D6KgNH26PG6tXq2H1QeVhjdu1CEvTy557ItFJu3bO/Dcc2Wu+ytWGJCVxc8FBZ4VK+SA+ZQpJqjVtRxMzWL3bs/GEn/4bv7ggzBcvSqPz3PmlNXRgoiIiMj3Avn8i6ihOINfg4qKCrzxxhvIycmBIAgYMGAAHnnkETz55JOYOHEidDodRFHE2rVrsXLlyipt9+7diw8//BA2mw1hYWGYMGEC/vSnP+Hhhx/GLbfcAgAoKSnBm2++iXPnzl3z3P/+97/x/fffAwA6d+6M+++/H08//TRSUlJcqaYHDx7Eu+++C7vdNyW1KDgNGGBFx47Se0oUBaxebaijRWgSReDZZ1tgyxad62f33VeBp57iSh/yrRtucHhcVqK8nKcEoWrSJBN0OmnBUmGh0lWysjlVZpEDwF13mXxWJvO3v61Anz5ylPP551vAZOKmU3l5CixcGI7k5GhMnNgKycnReO+9cOTlcdzwN2fOKPH99/LExLRpXBToDzz9jvX1d3NensK1bysA/O535Wjf3uHDHhERERHVT6CefxF5gu/aGmRmZqKwsBAAMGvWLDz++OOIj49Hv379kJiYiHnz5iEiQipb8tVXX+HChQsAALPZjE8//RQAEBkZiZdffhlTp05Fnz59cNttt+GJJ57AjBkzAEgZe5XHVtq9ezcOHjwIALj11lvx17/+FSNHjkTv3r0xduxYvPLKK+jSpQsAqZzntm3bmvgvQaFEEIDERHkyaMUKPbhN47Vefz0CmZlyAPTOO0145ZUSCJwHJj8QHu5ZcMTTdhT4oqJEjB8vj/3LlzfvAo/Tp5XYsUMuNTtrlu8yopVKIDW1GEql9OV37pwK77wTXker4GUySZvVDxzYGqmpkcjK0mH/fg2ysnRYsCASAwe2xnPPRcHMKtV+Y+VK+fPbo4cNvXqxrKI/CNTv5rfeikBFhTRl0LKlA48/zgVtREREFBgC9fyLyBMM8lXDZDLh22+/BQB07doVo0aNuuaY1q1bIykpCQAgiiL27NkDANizZw8qKioAAImJia7MO3fjxo1D9+7dAQBnzpzBlStXXI9t3rwZAKDT6XDfffdB8asNabRaLf7whz9A+CWasHv37ka9VqJfS0yUJ1dPnFBj0qRWXLXv5sMPw7BokbwvSUKCBf/8ZxGUSh92ishNfLxntRYTEnxQo5H8xvTpcpBvyxYtcnObb6xPS5Oz+Lp3t2HgQN++F3v1suMPf5Anst9/PxxHjoTeNtYmEzBrVgzS0sJq3MvCZhOQlhaGmTNjwCryvud0Sgu0Kk2dauQCJD8RiN/NJ0+qsHSpHDT+05/KERXF1X9EREQUGALx/IvIU6E9W1+DU6dOwfrLhjSDBg2q8bh+/fq5bl+6dAkAcPToUQCAQqHAgAEDamzbt2/fa9rabDb8/PPPAICbbroJYWFh1TVFbGws2rVrV6Utkbe0bu1Aq1ZyGZ79+zVctf+Lzz/X4+WXo1z3e/a04ZNPCqFr/sp2RDVKTjZCrW7YJJxaLSI5mftJhrLBgy1o21Ya+x0OAatW6eto4R1mM7B8ufxcs2ZV+EVQ4umny13lqx0OAc891wKOEKtQN3duFLKztQDqGk9EZGdrMXduVB3HUVPbu1eDCxekgLRCIeLuuxl59ReefDcDwL59ahQV+WZQfPXVSDgc0nN36mTHrFkVPukHERERkSc4N0KhhEG+auTl5bluX3/99TUeZzDIKxstFkuVtnFxcTUG6Wpqm5+fD/GX2oi1Pa97+8q2RN5QuWo/P7/mtLRQXbWflaXFU0+1cN3v0MGOJUsKEBnJFc3kX+LinEhKqjwprXtyHgCSkowe7+VHwUGpBKZNky9mMjIMzVKued06PYqKpO8cvd5ZpWS0L+n1It54o8R1/4cfNPjf/2o+rws2ubkKZGRUnqvWFWCQHs/IMIR8tr+vZWbKAfOhQy1o04bjur/w5LsZADZt0mPMmDjs3Kmp5Xjvy87W4Jtv5FVsL7xQCk3zdoGIiIioUTg3QqGEV+LVUKlU1d7+taKiItftFi1aVDm+tnYAUFxcXGPb+rSvfO7KtkTewFX71Tt4UI3f/76lq1xZdLQDaWkFnDwjvzVvXgkSEiyoz+R8QoIF8+eX1HEchQL5Agg4flyNgwfVTf6cS5bIi54mTzb51cKJYcMsVUpYL1gQgZyc0KjNnJ5uqLFEZ01sNgHp6c27nyPJTCbgiy/cS3X6R8CcZA35bo6MlM8xr1xRYvr0Vpg/PxLNsb7T6QT+/vdI1/1bbrHirrtCsIQHERERBTzOjVCoYJCvGi1btnTdPn/+fI3HnT592nW7co+9yrZXr16tNcuusq1Go0Hnzp0BAFFRUa699mp73tLSUuTn51d5XqLG4qp9IC9PgYULw5GcHO3ah/DllyOQkhKNigrpdRoMTnz2WSFuuCHE6rZRQNHrgcWLC5CSUlFjeQq1WkRKSgWWLClgyVkCAFx/vQODBsnnLvJ3QtM4flyFPXu0rvuzZvlfWZS5c0vRsqU03huNCrzwQlSzZDj62u7dnqXsZGcz1cdXvv5ah/Jy6VwlLMyJ8eMZlPE3Dflu3rfvCv7615Iqx33wQTgmTIjF8eNNu0fo2rV6HDwof5ZfeqnEL8ooExERETVUfc6/FArOjVDga9orhADVuXNnqFQq2O12ZGVlYciQIVAoqgYyHA4H1qxZA0AK1N1yyy0AgG7dumH37t2w2WzYtWsXRo0adc3v//nnn3Ho0CEAQP/+/aHVal2/5/rrr8fp06fxww8/oLi4uNpMvVWrVrnKet522231fl0vvvjiNT/TaDRITU0FALRq1arevytUVWZYxsbG+rgn3vfRRwqPVu2vXRuL558P7Iw2kwl4+mklPvtMuOZvkJUlf8MrlSIyMpwYN65FM/eQyDP/+x/wxht2/O9/CuzYoUBZGRAeLmLYMBEPPuhE69YaAME3npHnfv97Ad99J91es8aAf/5T02QXOq+9Jp9b9esnYsyYFn43kRwbC7z1lojf/la6v3mzDtu3x2HatOCL9BUUAFu2CNi0SYHduz37h7BYNE1yjhTM51/esnatnGWamAh07Mjzen/l/t2clSWgrAyIiMA1381/+xswcaId99+vwrFj0mfyp5/UuPPOWLzxhhOPPur0+phpNgOpqfIUwd13O3HnnS28+yQhiGMYEQUqjl8ULH59/nXqlIAzZ6QTqfbtgU8+0UAQ+D4PJqE2fgVPCo4XGQwGjB07FoAUkFu6dCmcTjmIYbVa8dFHH+HixYsAgKFDh7r23xs8eDBiYmIAAMuWLcPx48er/O6cnBwsWrTIFaQbN25clccnTZoEADAajVi0aBGMRnlVuyiK+Oabb7Bt2zYAQPv27XHTTTd562VTiNuxw7NZgqwsP5uRbSCTCZg4UYmPP647yNmlCzB0aPBN7FJwa90aeP55JzZuBL79Fli3zoHnn3eidWtf94z8UWKiCINBGueKiwWsXds0Y3xFBbBkiXwa+vDDDr8L8FVKSRExerR8Hvjkk0r87W8K3HmnEkOHKnHnnUq88YYCV682bT+uXgVef917z2u1Atu3C3jpJQUSEpT4zW9USElR4X//U8Bq9ewfIyLCs75Q41y5Anz9tfxvNnNmYC++CgWV383r1jmwY4ejxu/mfv2A3bvtePRRuYKE2SzgqaeUmDRJiStXvNuvf/9bgXPnpPeSSiXilVdYuYKIiIiCg/v515YtdtfPz58XcPKkDztG5AVCRUUFZ6yr4XA48P7772Pv3r0AgHbt2qFXr14oLy/H4cOHUVIi1ehVq9VYsGABoqOjXW0vXryIt99+G4WFhVAoFOjbty/i4uKQk5ODn376CQ6HdLHUp08f/OlPf7rmuTds2IDly5dDFEVERkbilltugSAIOHbsGC5duuQ67rHHHsPAgQMb/VoNBqkc1+XLl13BR6peZfQ/Ly/Pxz3xvokTW2H//oaX2erf34ovvshvgh41j+eei0JaWhikfQhrm9SUHk9JqUBqKmt0U+AJ5vGLvOvJJ1tgxQrp3GDkSDOWLCn0+nMsW6bHM89IJc7Dw53Yv/8qwsL89xzk7FklRo2Kg8VS8/eEWi0iKcmI+fNLvJr9aDJJe+ZmZFS/T159n1cUgZ9/VmH7di22b9di924NjEbvrvebM6cUs2eXe/V3Ahy/6vLBB2GYP1/aJ7ldOzt2786Fgks5g87mzVo8/XQL5OfLWZvR0Q689VYxxo1r/GZ9RUUCBg9ujZIS6c3z29+W4+9/L2307yWOYUQUuDh+UTAbMyYWR49K+9DPm1eC3/++wsc9Im8KpPFLEAS0bdsWAKokfDUEy3XWQKlU4rHHHkN2djY2bdqEs2fPIicnBwBc++YBwNixY6sE+AApw27+/PlYt24d9uzZg/3797vaVQbRFAoFpk2bVu1zjx8/Hl26dMFXX32Fn376CVu3br3meTt37uyVAB9RpfBwz1Z9e9rOH3i6D+Gzz5YhNjZwXzcRUW2mTze6gnzbt2tx+bICbdt6d8xbvDjMdTsx0eTXAT4AaN3agdatHTh/vuZTZ5tNQFpaGE6fVmHx4gLo9Y1/XpMJmDUrBtnZ2hqPqe15CwoU2LlTg+3bddi+XYsrV5Q1/h4AiItzYNgwC/r2teLll6Ngt9c/o0+tFpGc7H/7KoaCys8rIH2eGOALTqNHW7B5cx6eeaYFNm2SIvqFhUo8+GAMUlIq8PLLpa5MbE8sXBjhCvBFRDjxpz95P2BPRERE5C9GjjS7gnzbtmkZ5KOAxiBfHRISEpCQkAC73Q6j0Qi73Y4XX3wRZrMZERERmDBhQrXtwsPDkZSUhKSkJJjNZthsNnz33XdYvHgxAKnEZ/v27Wt83i5duuDJJ5+E0+mE0WiEUqnEm2++iVOnTgEAkpOTvf9iKaTFx1ur7D9XXwkJ1iboTfNIT68+K6I2NpuA9HRDk2QqEBH5g/h4K667zo7z51VwOgWsWGHAE094b8z78Uc1fvhBzhyfOdP/L6bmzo36JcBXd9Z3drYWc+dGeSXre+7cqF8CfPV73pdeisKUKSZkZWmRlaXFjz/WnqGv04mIj7dg2DDpvx497K6yqUePqhuU6Z6UZOQCGB84ckSFn35Su+4nJjLQGsxatXLi008LsXixAfPmRcJsloJyaWlhyM7W4l//KkKfPrYG/95z55T49FN58cXjj5cjOpqfZyIiIgpeI0da8O9/S/sNZGdrYTLBKws1iXyB6zzrSaVSITIyEl9//TXMZjMA4J577nGVuqyNTqeDTqfDV199BQDQ6/VITEys1/MqFAqEh4fj+PHjrgDfrbfeiq5du3r4Soiql5xshFrdsNW/KlVgr9rfvbvh5UkBIDvbs3ZERIFAoQCSkuSxfflyA7xZzXvxYvnc6ZZbrLjxRnstR/uep1nfeXmNO8325HnT0w2YPr0VFi2KqDHA16uXFX/8YxmWLcvHkSOXkZZWiD/8oQI9e9qr7Is4b14JEhIs9XruhAQL5s9nKWtfWLlS/jz162dFly7cQy3YCQJw331GbNyYj5tvlhfbnT6twqRJrfDee+FwNPBt8MYbka6Fb7/5jR2/+x0XsxEREVFwGzjQ6qpOZjYLtVZPIfJ3zORrgOLiYmzZsgUA0KFDBwwfPrzebbdt24bCQmlPm8mTJyMyMrJBz7169WoAgEajQVJSUoPaEtVHXJwTSUnGeq7al508qUJsbGBm85WXezYB62k7IqJAMXWqCW++KZ2rnDmjwvffqzFwYMOzQ36trEzA6tXy8shZs/w/i8/TrO977olBly52qFRSKUulElCrpQUyKpX8f7W66v3K/+/cqWnw81b33d2mjcOVqTd0qAWtWtUvO0evBxYvLqh1P0BAxNSpRixY4N19CKl+7HZg1Sr58zR1auAuvKKG69LFjrVr8/HWWxFYtCgcoijAbhewYEEktm3T4r33itG+fd3Rvv371Vi7Vn4fzZlTxlXsREREFPTUamDoUAvWr5dOfLZu1WLUqMbvc0zkCwzyNcBXX30Fq1UKZsycOROKem54YbFY8OWXXwIA2rZtizFjxjToefft24dz584BACZMmICYmJgGtSeqr3nzSnD6tKreq1fsdgEzZ8bggw8KMXZs4H0RhuI+hERE9dGhgwODB1uwa5f0fZCRYcDAgY3P1Fq1Sg+jUTp/atHCiQkTTI3+nU3N06zv06fVOH1aXfeBXqZQiBgxQi7B2a1b1Qy9htDrgdTUEjz7bBnS0w3IztagrEyBw4fVvwT9BPTpY2eAz0eysrTIy5P2WVSrRUya5P+fJ/IujQZ44YUyjBhhwezZLXDpknR5v2ePFmPGxOK110pwzz3y+yIvT4GlSw3YvVuD8nIFwsOdOH1anhLo1cta5XgiIiKiYDZihBzk27JFh7//vdTHPSLyDNNR6unKlSuuLL6EhAR079693m3XrVuHkhJpYiwlJQUqVf1jq3a7HRkZGQCA2NhYjB8/vgG9JmqYylX7KSkVNZbuVKtFjBxphlYrBbosFgG//3001qwJrBk+pxMeZEhIAnkfQiKi+po+Xc4KWrtWD6PRw0jRL0QRWLxY3vNp2jRjQGSLBFr2dp8+NixeXIiHHqpA9+6eB/jcxcY6MXt2OdLTC/Hll/lVNqVfvjwA/hGD1IoV8t9+zBgzoqO9WFeXAkpCghWbNuVhyhR53C4rU+CJJ1riscda4OpVAc89F4WBA1sjNTUSWVk67N+vQVaWDhcvytemzz1XinquYyUiIiIKeCNHml23z55V4cwZpQ97Q+Q5ZvLVU3p6OhwOB3Q6HaZPn17vdgUFBVi/fj0AYMCAAejVq1eDnvebb77B1atXAUgBQo2Ge4FR06pu1X7lSt+EBCuSk42IjXVizx4N7r8/GmVlCtjtAh57rCXKy0uQkuL/paLKygTMnt3Co3rbanVg70NIRFRfd95pxosvOlFerkB5uQLr1+uQmOh5hsf+/WocPSpnts2cGRhjqafZ21262DBzphF2u5T5brdLi0scDun/7j+32wXYbO73gb17NSgqavhFZkRE02ebT59uxH/+Ew4AOHxYg8OHVejVy7/3Vgw2paUCNm50L9XJ7KtQFxUlYtGiYowebcGLL0ahrEyK1n3+uQHr1ulhtdYd8f/PfyJw220FAbEAg4iIiKix2rVzont3G44fl65Tt23T4vrrA+M6lcgdg3z1cPjwYRw8eBAAMGXKFLRo0aLebTMyMmC1WqHRaJCcnNyg5y0tLcXatWsBAH379kXfvn0b1J6oMSpX7c+eXf3jt95qRUZGAWbMiEZRkRKiKOC551qgvFzAH/7gv3ssnT2rxIMPRuPEiV+XUKtrH0Lp8aQkKchJRBTs9Hqp/N/SpVL23fLlhkYF+dyz+BISLOjSJTCCQvHxVmRlNTxbPTHRhIce8vz7cOHCcKSmNmwPZ6B5ss27drWjf38r9u+XFp9lZBjQqxdL2zSnr77Sw2yWzltatnRg1ChzHS0oVNxzjwkDB1oxe3YLfPedtKBNCvDVfa6bna3F3LlRSE1tfHlmIiIiokAwcqTFFeTbskWHBx9kkI8CD4tx1MHhcGDp0qUAgHbt2mHs2LH1bnvy5Ens2bMHADBp0qQG76W3cuVKmEwmqNVqpKSkNKgtUXPo3duGVasK0KaNw/Wz+fOj8OabERD9sGJUVpYGd90VWyXAN3KkGQMHWlD7pAcACEhIsGD+fE56EFHoSEqSL3B27dLiwgXPypcUFQn44gs5NWTWLP9dDPJrycnGGktY18QbWd++et76ci/numqVHpbA25o3oLmX6pwyxQQW+yB3HTo4sGJFAR57rAxScA+oz7kuIAXt8/I4TUBEREShwb1k57ffamDm2jkKQDx7r4NSqcRrr72GTz/9FK+++iqUyvpPbnXt2hWffvopPv30U0yYMKHBz/3ggw/i008/xYcffojY2NgGtydqDt262bFqVT6uu07OyHjnnQi8/HKk3wT6RBH46KMwpKTEoLhYHvYef7wM//d/hUhPr3sfwpSUCixZUgBdYG09SETUKAMG2NC5szy+Z2Z6VsNtxQqDK+soJsaBO+4InCunuDinW7Czri826XFvZH376nnra9IkE3Q66bmKipT45ht+QTaX8+eV2L1bLjnOUp1UHaUSCAurK3vvWjabgPR0Q9N0ioiIiMjPDBxohcEgXdeYzQrs2dPwrX2IfI1BPiJqtI4dHVi1Kh9du9pcP/voo3D8+c9RcDhqadgMLBbgmWdaYO7cKDid0iSHTufEv/9diBdeKINSKe9DuHfvVcyZU4phw8zo39+KYcPMmDOnFHv3XkVqagkDfEQUcgShajZfZqYBzgbGkEQRWLxYnjBOTjYGXNbRvHklSEho/qxvXz1vfURGirjzTjlYu3w5gwLNZeVKOdjepYsNffrYajmaQtnu3Z4NttnZATZIExEREXlIqwWGDJHLkmzZwiAfBR4G+YjIK9q2dWLVqgL07i3vBbRsWRj++MeWsDb99kDVunpVgalTW1WZeGzb1oHPPy/A5MnXZpFU7kOYnl6IL77IR3p6IWbPLucefEQU0qZONUKhkDLFzp9XNXjSePduDU6dksskz5gReHsc6PXA4sXNn/Xtq+etL/eSndu2aXH5Mi8tmpooSpmxlaZONUFoWKIWhZDycs8+k562IyIiIgpEI0fKQb6tWxnko8Cj8nUHiCh4REc7sXx5Ae6/PxrffSd9KX75pR5Go4D//rcIen3z1e/84Qc1fve7aFy5IpfYHTjQgg8/LGLQjoioAdq2dWL4cAu2bpUiSMuXG3DbbfVfveGexTdihBkdO/o4xdtDlVnfzz5bhvR0A7KzNSgvVyA83ImEBCuSk5umVKavnrc+brvNig4d7LhwQQWnU8DKlQY8/ni5T/oSKr7/Xo2zZ6VLOEEQcc89LNVJNQsP92xs8LQdERERUSByD/KdOqXG+fNKXHddYF63UmjiEj0i8qrISBFLlxZW2bh2yxYdZs6MRllZ8yw1X7VKj3vuaVUlwJeSUoGMjAIG+IiIPDBtmpyx9dVXOpSX1288z89XYN06ubTgrFmBl8X3a77K+vbHbHOFomo51+XLDX6zH2+wcs/iu+02K9q14+QD1Sw+3rNyGgkJPirDQUREROQDHTo40KWLXAKfJTsp0DDIR0Rep9eL+OSTQtx5p7y6fPduLaZPj0FhYdMF+hwO4JVXIvHEEy1hsUjPo1SKePXVYixYUBJwe0AREfmL2283IypKCiaZTAp8+WX96kIuX26AzSaNx23aODBmzLWlkimwTZsmf9efPq3C99/zy7apmM3AF1/IQfOpUwM/aE5NKznZWGOp35qo1SKSk/neIiIiotDins23bVsz74NA1EgM8hFRk9BogP/8p6jKCv+DBzWYOrUVrl71/tBTUiLggQei8Z//hLt+1rKlA+npBXjgASP3qyEiagSdDpgyRQ7muO91WhOnE0hLk4+bMcMIFQvFB50OHRxVNqpfvlxfy9HUGJs26VBSIp1D6fVO3Hkng+ZUu7g4p9u5eF3BPunxpCTflQAmIiIi8pVRo+Rrml27NLBYajmYyM8wyEdETUalAt56qxi/+528P8/x42rcfXcrnD+vrKVlw/z8sxITJsRiyxZ5pU3PnjasW5ePwYNZboiIyBvcF218950WZ87UPo7v2KHFuXNSVE+hEJGcXNGk/SPfmT5dfm+sXSvtxUve516q8847zQgPZ21Uqtu8eSVISLAAqOtzKSAhwYL580uao1tEREREfmXQIAv0emmhk9GowJ49rFBCgYNBPiJqUgoFMG9eKZ58ssz1s3PnVLj77lb4+efGp3Rs2aLFhAmxOH1a/l133GHCmjX53CSXiMiL+vSxoXt3eZ+CjIzas/kWL5YfHzPGjN/8hpkhweqOO8yIiJD+fSsq6l/OleovP1+BrVvlvUFYqpPqS68HFi8uQEpKRY2lO9VqESkpFViypAA6fnyJiIgoBOl00p7XlbZu5UkRBQ4G+YioyQkC8NxzZXjpJXll8JUrStx9dwx+/FHt0e8UReA//wnDffdFo6xMHsqeeaYU//1vEcLCuLqdiMibBKFqNl9mpgGOGtZSXL6swNdfyxdFs2YxIBHM9HoRkyfL5VzrCgBTw61Zo4fdLu9vyUoF1BB6PZCaWoK9e69izpxSDBtmRv/+VgwbZsacOaXYu/cqUlNLGOAjIiKikDZqlFwOf9s2bS1HEvkXBvmIqNk88kgFFiwohiBIAbjCQiWmTYvB3r0NS4E3mYDZs1vglVeiIIrShJfB4MR//1uIp58uh4IjGxFRk7jnHhOUSmkMv3xZiZ07q7/wWbbMAIdDGp87dLBj+HBuaBDs3Et2Zmdrcfas98pyE7BihbzXYWKiEUr+eckDsbFOzJ5djvT0QnzxRT7S0wsxe3Y59+AjIiIiAjBypHzdeuKEGjk5POmmwMCpcCJqVjNnGvGvfxW7JonLyhRITo7G9u3SRHFengILF4YjOTkaEye2QnJyNN57Lxx5edJwdemSAomJrbBqlZwl0KGDHWvW5OOuu8zXPiEREXlNXJyzyobkGRn6a46x24G0tDDX/ZQUBiRCQb9+NnTtWv9yrlR/x4+rcOiQvCAqMdFUy9FEREREROSJjh0d6NzZ7rq/ZQuz+SgwMMhHRM1uyhQTPvqoEFqtFOgzmRS4//5oJCdHY+DA1khNjURWlg7792uQlaXDggWRGDiwNX7725a4445YHDwoT3QlJFiwbl0+brzRXtPTERGRF7lnbG3YoEdJiVDl8S1btLh8WYrqqVQi7r2XpTpDgSCgyr91Zqa+xnKu1DDuWXy9e1vRvTvPeYiIiIiImsLIkXICgfue2ET+jEE+IvKJceMs+OyzAhgMUnkgm01AVpYONptQ7fE2m4CNG/XIz5fTQR54oALp6QWIjmaJISKi5jJ6tBnR0VL0xmwWsGZN1Wy+xYvlLL7x480sAxdC3Mu5XrqkqrGcK9Wfw4Eq1QumTmUWHxERERFRU3Ev2blzpxZWboVNAYBBPiLymSFDrFi2rABqtdigdgqFiNTUYrz6agnU6ibqHBERVUujAe6+Ww40uJdlvHBBWWW146xZFc3aN/KtuDgnRo+WV74uX35tOVdqmF27tLhyRc6MnTKFQT4iIiIioqYSH2+BTifNU1ZUKLB3r6aOFkS+xyAfEflUhw4OiA2L8UEQgHHjuP8eEZGvuJfsPHBAg5MnVQCAtDQDRFHKyO7c2Y7Bg7nsMdRMny4HoTZs0KO4uPoMfaqfzEw5UDpqlBkxMcyMJSIiIiJqKno9cNttcjbf1q06H/aGqH4Y5CMin0pPN8Bub9gEoMMhID3dUPeBRETUJG66yY5eveQA3syZ0ZgwoRXefz/c7WcVEBjfCTmjR5sREyOVc7VYBHz+ObP5GiIvT4GFC8ORnByNu+5qVeXvx1KdRERERERNz71kJ/flo0DAIB8R+dTu3Z6lvWdnM12eiMhXTCZA63atc/GiCgcOaNz2VRXx008qmJl0HXLUaiAxsfpyrlQzkwl47rkoDBzYGqmpkcjK0uGHHzRwOuXP1NatWn6miIiIiIia2IgR8kn3sWNq5OQwhEL+je9QIvKp8nLPhiFP2xERUeOYTMCsWTHYt6+2xRYCVqwIw8yZMTAx+SjkuJdzPXhQg6NHVT7sjf+r/EylpYW5Bcp/TUB6Oj9TRERERERNrXNnBzp1srvub9/Okp3k3zhLTkQ+FR7u2d4ynrYjIqLGmTs3CtnZWgB1bagqIjtbi7lzo5qjW+RHevSwo08fuZzr8uXM5qsNP1NERERERP5l5Eg5m48lO8nfMchHRD4VH2+t+6BqJCR41o6IiDyXm6twK79Y14Z70uMZGQbk5fGUM9S4Z/OtWqWHzebDzvgxfqaIiIiIiPyP+758O3ZoeT1Dfo1Xh0TkU8nJRqjVda1cr0qtFpGcbKz7QCIi8qr0dEMt5QSrZ7MJSE9nJleomTzZBK1W+n4vKFBi8+bAKnGTl6fAwoXhSE6OxsSJrZCcHI333gv3enCNnykiIiIiIv9z221W1/VMWZkC339f23YVRL7FIB8R+VRcnBNJSZUBu7rLVAFAUpIRsbEs10lE1Nx27/bswiY7mxdEoaZFCxF33CFvHrdsWWAEpUwm4LnnojBwYGukpkYiK0uH/fs1yMrSYcGCSAwc2BrPPRcFs7nu31Ubsxk4flyFL7/Ue9SenykiIiIioqaj14uIj5ez+Viyk/yZytcdICKaN68Ep0+rftmPpjYCEhIsmD+/pFn6RUREVZWXvqq87gAASlRJREFUe7Y+zNN2FNimTzfh88+l4N6WLVrk5ioQF+e/i3RMJmDWrJhaz0dsNgFpaWE4fVqFxYsLoK8lRme1AufPK3HmjAqnT6tw5kzlf0pcuqSEKDYsg88dP1NERERERE1r5EgLtm+XKpJs3arDiy+W+bhHRNVjkI+IfE6vBxYvLsDcuVHIyKi+bJVaLSIpyYj580ugC6yKX0REQSM83LMAjaftKLANHmzBb35jx6VLKjgcAlau1OPRRyt83a0azZ0b9UuAT0Tt++OJyM7WYu7cKLz2WgkuXlS6BfGUrmDehQtKOJ2eB/Jqw88UEREREVHTGjnSjJdfjgIA/PSTGleuKNCmDc/Dyf8wyEdEfkGvB1JTS/Dss2VITzcgO1uD8nIFwsOdSEiwIjmZJTqJiHwtPt6KrKyGr7RISLA2QW/I3ymVQFKSCe++GwEAWL7cgEceqYDQNHGvRsnNVSAjo7KkaF0dlB5PSzNg2TIDHA7PXpBKJSIiwomiImWD2/IzRURERETUtG64wYHrrrPj/HkphLJtmxb33muqoxVR82OQj4j8SmysE7Nnl2P2bF/3hIiIfi052Yh33omoNuO6Jmq1iORkY90HUlBKSjK6gnwnT6px4IAa/fvbfNyra6WnV19JoHYCHI7aj1AoRHTo4EDnznZcf70d11/v+OX/drRv70BhoQKDBrXmZ4qIiIiIyM8IAjBihAWffSaFULZs0THIR36JQT4iIiIiqpe4OCeSkoxISwtDfUoaAgKSkpiJHco6dnQgIcHi2udu2TID+vf3v711d+/WeNxWEES0a+eoEsCr/O+66xzQ1PKr+ZkiIiIiIvJfI0ea8dlnYQCAHTu0sNsBFSMq5Gf4liQiIiKieps3rwSnT6tcQZuaCUhIsGD+fP8L6FDzmj7d6Hq/rF2rx7x5pdDrRR/3qqrycoVH7W680YYvvshr1H7B/EwREREREfmnIUOs0GhEWK0CSksV2L9fg0GDWDqf/ItnV7NEREREFJL0emDx4gKkpFRAra4+UKNWi0hJqcCSJQWNCn5QcLjrLjPCw6XMs7IyBdav9783RWX/GqpVK0ej3+P8TBERERER+SeDQcStt8pBvS1b6lqYR9T8mMlHRERERA2i1wOpqSV49tkypKcbkJ2tQXm5AuHhTiQkWJGczHKCJDMYREyaZMLSpVKZm2XLDLjnHv/ayyI+3oqsrIZHzxISvLOKl58pIiIiIiL/NGKEGTt2SMG9rVu1eP75Mh/3iKgqoaKiwr9q5VCzMxgMAIDLly9DFPl2qE1sbCwAIC8vz8c9ISJqGI5fRORLe/eqMWVKrOv+7t1X0aGDo15tm2P8ys1VYODA1rDba9sTryq1WsTevVcZfCOiWvEcjIgCFccvIsmJEyqMHBnnun/gwBXExfEawJ8F0vglCALatm0LADAajR79DpbrJCIiIiKiJjVggA033GBz3c/M1PuwN9eKjHQiIqK+F+rSorikJGbXEREREREFu65d7WjXzu66v20bS3aSf2GQj4iIiIiImpQgANOnyyU6ly83wOlH8bGXX45CUZGynkcLSEiwYP78kibtExERERER+Z4gACNHWlz3t27lJtnkX7gnXx0cDgf27NmDvXv34syZMygvL0dUVBRuvPFGjBs3Dh06dKixbXFxMbZu3Yoff/wRly9fhtPpRNu2bTFgwACMHj0aen3tK5h3796NrKwsXLhwAUajEREREWjZsiV69eqFcePGISIiwtsvl4iIiIioSSQmGvHGGxFwOgVcvKjCt99qMGSId/a0a4zPP9dj8eIw1/1u3Ww4c0YFm+3a0p1qtYikJCPmzy+Bjtf2REREREQhYeRIC5Yska4ZsrK0sNsBFSMr5Ce4J18tcnNzsXDhQuTk5FT7uCAImDhxIu6++24IQtVJgF27duHTTz+FzWartm1UVBQef/xxdO3a9ZrHnE4nPvzwQ2RnZ9fYt7CwMDzxxBPo0aNHA15R9bgnX/0FUj1fIiJ3HL+IyB/MmhWNLVuk6Ng99xjxz38W19mmKcevn39W4s47Y1FRIRU46dPHitWr81FaqkB6ugHZ2RqUlysQHu5EQoIVycks0UlEDcNzMCIKVBy/iGTl5QJ69WrjWgj4+ed5GDiw+nl/8r1AGr+8sScfg3w1qKiowEsvvYTCwkIIgoBbbrkFAwYMgFarxenTp/HNN9/AbDYDACZMmICpU6e62u7duxeLFi0CIAXjRo4cia5du6KiogL79u3Dvn37AABarRYvvvgiOnbsWOW5165di1WrVgEAEhIS0LdvX2i1WpSWluLw4cPYt28fHA4HdDodUlNTERkZ2ajXyiBf/QXSAEFE5I7jFxH5g6++0uHhh6MBADqdiAMHriAysvbzz6Yav0wmARMntsLRo2oAQFSUExs25OG66xxefR4iCm08ByOiQMXxi6iqadNi8O230n58Tz1VhmefLfNxj6gmgTR+eSPIxz35apCZmYnCwkIAwKxZs/D4448jPj4e/fr1Q2JiIubNm+cql/nVV1/hwoULAACz2YxPP/0UABAZGYmXX34ZU6dORZ8+fXDbbbfhiSeewIwZMwAAFovFdWwlm82G9evXAwBGjRqFP/zhD7j11lvRt29fDBs2DH/84x/x+OOPu55ry5YtTf2nICIiIiLyirFjzWjZUgqimc0C1q6tvXx9U3rppUhXgA8A3nmnmAE+IiIiIiKq1qhRZtftrVu1PuwJUVUM8lXDZDLh22+/BQB07doVo0aNuuaY1q1bIykpCQAgiiL27NkDANizZw8qKioAAImJia6osbtx48ahe/fuAIAzZ87gypUrrscuXboEk8kEABgyZEi1/evXr58rwHjp0iWPXiMRERERUXPTaIB77jG57i9bZvBJPzIz9UhPl/fhe/jhctx+u7mWFkREREREFMpGjrS4bh88qEF+PkMr5B/4TqzGqVOnYLVaAQCDBg2q8bh+/fq5blcG244ePQoAUCgUGDBgQI1t+/bte01bAHA4HNXedme3212pm2FhYdUeQ0RERETkj6ZPl0uQHDigwcmTzbtj/fHjKrzwQpTrfv/+Vrz4Ymmz9oGIiIiIiAJL9+52tGkjz9dv28ZsPvIPDPJVw71W6/XXX1/jcZV72QFS6U33tnFxcbUG4KprW9lOqVQCALZt21Zt2507d7oCgD169KjxOYiIiIiI/M1NN9nRq5fVdX/58ubL5quoEPCHP7SEySRdBrVo4cT77xdBra6jIRERERERhTRBYMlO8k8M8lVDpVJVe/vXioqKXLdbtGhR5fja2gFAcXHxNW0BIDw8HPHx8QCAXbt24dNPP0VOTg6sVityc3PxxRdfIC0tDQDQsWNH3HLLLfV6TURERERE/uLee+VsvhUr9LDZmv45RRF44YUonDwpR/Tee68I7dpxHz4iIiIiIqqbe8nO7du1qKEQH1Gzat7aOAGiZcuWrtvnz59Hp06dqj3u9OnTrtuVe+xVtr169SosFgu02uoj+pVtNRoNOnfuXOWx+++/HwqFAjt37sS2bduqzejr1asXHn744TqDiURERERE/mbKFBPmz4+C1SogL0+JrVu1GDfOUnfDRkhPN2DlSjlr8LHHyjB6dNM+JxERERERBY8hQyxQqUTY7QKKipQ4eFCN/v2bYcViAMnLU2DpUgN279agvFyB8HAnEhKsSE42IjbW6evuBSVGiKrRuXNnqFQq2O12ZGVlYciQIVAoqiY9OhwOrFmzBoAUqKvMqOvWrRt2794Nm82GXbt2YdSoUdf8/p9//hmHDh0CAPTv3/+aQKBGo0F8fDx++OEHlJWVXdO+ZcuWGDJkCMLDwxv0ul588cVrfqbRaJCamgoAaNWqVYN+XyiqDKrGxsb6uCdERA3D8YuI/ElsLDBxooiVKwUAwOeft0RKSvXLYL0xfh06BLz0knzpM2SIE//4hw4qlc7j30lEVB88ByOiQMXxi+hasbFAQoKIHTuk65g9e6Jx++0MXAGAyQQ8/bQSn30mwGYTqjyWlaXD229H4P77nXj7bSd0TXwZFmrjF8t1VsNgMGDs2LEApIDc0qVL4XTKH1ar1YqPPvoIFy9eBAAMHTrUtf/e4MGDERMTAwBYtmwZjh8/XuV35+TkYNGiRRBFEQAwbty4a55/x44dePPNN6sN8AFSmdD3338fqampVfbzIyIiIiIKFA88IJ9ff/WVgNzcpnme0lLg3ntVMJulC83YWBGLFzvAghhERERERNRQt98uum5v3CjUcmToMJmAiROV+PhjxTUBvko2m4CPPlJiwgQlTKZm7mCQEyoqKsS6Dws9DocD77//Pvbu3QsAaNeuHXr16oXy8nIcPnwYJSUlAAC1Wo0FCxYgOjra1fbixYt4++23UVhYCIVCgb59+yIuLg45OTn46aef4PilWG+fPn3wpz/9qcrz5uXl4S9/+QusVisAoE2bNrjtttvQoUMHlJeX47vvvsOPP/7oOn7s2LFISUlp1Gs1GKSyRZcvX3YFH6l6ldH/vLw8H/eEiKhhOH4Rkb9xOIBBg1rjyhUlAGDu3BI8/HDFNcc1ZvwSReCPf2yJtWv1AABBEJGWVojhw7lQjoiaB8/BiChQcfwiqt6RIyqMGxcHQLq+OHToKqKjQzub77nnopCWFgZABFBb4FN6PCWlAqmpJU3Wn0AavwRBQNu2bQEARqOxjqOrx/WrNVAqlXjssceQnZ2NTZs24ezZs8jJyQEg/eErjR07tkqADwDat2+P+fPnY926ddizZw/279/valcZRFMoFJg2bdo1z7t9+3ZXgO+2227DAw88AI1G43p86NCh+Pbbb/Hhhx9CFEVkZWUhMTERuqbOcSUiIiIi8iKlEpg2zYh//jMCALB8uQEPPVQBwYuLYT/7zOAK8AHAk0+WM8BHREREREQeu/FGO9q0ceDKFSVEUcD27VrcfXfopqbl5iqQkVG593ldF3PS4xkZBjz7bBn36PMSBvnqkJCQgISEBNjtdhiNRtjtdrz44oswm82IiIjAhAkTqm0XHh6OpKQkJCUlwWw2w2az4bvvvsPixYsBSMG69u3bX9PuxIkTAIDIyMhrAnyVbrvtNpw+fRqbNm2CxWLBlStX0KlTJ++9aCIiIiKiZpCUJAf5jh1T49AhNfr08c7G9YcOqfHyy1Gu+7fdZsHTT1dfDp+IiIiIiKg+BAEYMcKMZcuk7bu2bAntIF96uqHGEp01sdkEpKcbMHt2eRP1KrRwT756UqlUiIyMxNdffw2z2QwAuOeee1ylLmuj0+mg0+nw1VdfAQD0ej0SExOrPbayDGi3bt2qDfBVuvnmm123S0tL6/06iIiIiIj8RefODgwaJGfWLV9e97l1fZSWCnjkkZawWiv34XNg0aIiKJVe+fVERERERBTCRoyQr2G2b9fCGcIJabt31xzDqE12tmft6FoM8jVAcXExtmzZAgDo0KEDhg8fXu+227ZtQ2FhIQBg8uTJiIyMrPa42gJ77hQK+Z+uPoFGIiIiIiJ/dO+98r4Dn3+uxy/r6TwmisAzz7TAuXNS0RKFQsSiRUWIiwvhK28iIiIiIvKaYcMsUCqlbbkKCpQ4dEjt4x75Tnm5ZyEmT9vRtfiXbICvvvrKtV/ezJkzqwTaamOxWPDll18CANq2bYsxY8bUeGyrVq0AAPn5+bX+zrNnzwKQgn1t2rSpVz+IiIiIiPzNhAlmGAxSAK6kRIGNGxu31/THH4dh3Tp5H75nninD4MHWRv1OIiIiIiKiSlFRIm65Rb7G2LpV68Pe+FZ4uGeLKT1tR9dikK+erly54sriS0hIQPfu3evddt26da4ynCkpKVCpat4KsUePHgCAc+fOIS8vr9pjTCYTduzYAQC48cYbER4eXu++EBERERH5k7AwERMmyOl7jSnZuX+/Gq+8IlfMGD7czH0eiIiIiIjI60aOlEt2bt3auIWKgSw+3rMFlQkJXIjpLQzy1VN6ejocDgd0Oh2mT59e73YFBQVYv349AGDAgAHo1atXrccPGTIE0dHREEUR//rXv1wZe5XOnj2L9957D7m5uVAqlZg2bVqDXwsRERERkT9xL9mZlaVFTk7DN88rKpL24avc9L1NGwf++c9i1LP4BhERERERUb25B/kOHFCjqEjwYW98JznZCLVabFAbtVpEcrKx7gOpXmpOKSOXw4cP4+DBgwCAKVOmoEWLFvVum5GRAavVCo1Gg+Tk5DqPDwsLw5NPPomFCxfi3LlzePnllxEZGYnw8HAUFxfDaJTe/EqlEg8++CA6duzo0WsiIiIiIvIXgwZZ0amTHWfPqiCKAjIz9Xjqqfpn4Iki8Kc/tUROjnR5o1SK+Pe/ixATwxIwRERERETkfTfdZENsrAN5eUo4nQKysrSYPLmRG4wHoLg4J5KSjEhLCwMgAqgt2Ck9npRkRGwsr9W8heta6+BwOLB06VIAQLt27TB27Nh6tz158iT27NkDAJg0aRJiYmLq1a5jx4549dVXMX36dPTo0QOiKOLq1atwOBxo06YNhg8fjvnz52PIkCENf0FERERERH5GEIDp0+WVnBkZBjgbcM33wQdh+OYbuUTOnDlluPVWln8hIiIiIqKmoVAAI0awZCcAzJtXguhoB2oP8AGAgIQEC+bPL2mOboUMoaKiomG5lBR0DAZp35PLly9DFPl2qE1sbCwA1LhfIhGRv+L4RUT+7tIlBW69tTWcTunCcMWKfCQkWOscv/bu1SAxMQYOh9Ru9GgzPv20kGU6icgv8ByMiAIVxy+iuq1Zo8Mf/xgNAIiNdWD//qsheR3y7bcaTJvWqtZj1GoRSUlGzJ9fAl0Tx0MDafwSBAFt27YFAFcVx4ZiuU4iIiIiIvK53/zGiWHDLNi2TbriW77cUOdm7AUFCjzySEtXgO83v7Hj3XeLQvLCmoiIiIiImtewYRYoFCKcTgF5eUocOaLGzTfbfN2tZuVwAH/7W5Trfu/eVtxxhxnZ2RqUlysQHu5EQoIVycks0dlUGOQjIiIiIiK/MH260RXk+/JLHV55RcAvizCv4XQCs2e3wJUrSgCASiXi/feLEB3NyhRERERERNT0WrYU0a+fDfv2aQAAW7ZoQy7It3SpAUePql33X3+9BH372jB7tg87FWK4xpWIiIiIiPzCuHFmtGghre40mRT44gt9jcf+61/hroAgAPzlL6W45ZbQuqAmIiIiIiLfGjnS7Lq9davWhz1pfiUlAlJTI1z3p041om9fXpM1Nwb5iIiIiIjIL+h0wJQpJtf95curD/J9+60G//iHfDE5frwJDz1U0eT9IyIiIiIicjdqlMV1e98+DYqLBR/2pnm9+24ECgulyioGgxMvvFDq4x6FJgb5iIiIiIjIb9x7r7zZ+N69Whw/XvXxvDwFHnusJZxO6eK5Qwc73nqrGELoXEsTEREREZGfuPlmG2JiHAA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      "text/plain": [
       "<Figure size 1800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fis,ax = plt.subplots(1,1,figsize=(12,4),dpi=150,facecolor='whitesmoke')\n",
    "ax.plot(huozai_df, marker='o', ls='-', color='blue')\n",
    "ax.xaxis.set_major_locator(ticker.MultipleLocator(10))\n",
    "ax.set_ylabel(ylabel='# of fires', fontsize=17)\n",
    "ax.set_xlabel(xlabel='year',fontsize=17)\n",
    "plt.xticks(fontsize=15)\n",
    "plt.yticks(fontsize=15)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4a07df49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lb_stat</th>\n",
       "      <th>lb_pvalue</th>\n",
       "      <th>bp_stat</th>\n",
       "      <th>bp_pvalue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>40.299773</td>\n",
       "      <td>3.977145e-07</td>\n",
       "      <td>37.369023</td>\n",
       "      <td>0.000001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>49.014158</td>\n",
       "      <td>2.079792e-06</td>\n",
       "      <td>44.209654</td>\n",
       "      <td>0.000014</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      lb_stat     lb_pvalue    bp_stat  bp_pvalue\n",
       "6   40.299773  3.977145e-07  37.369023   0.000001\n",
       "12  49.014158  2.079792e-06  44.209654   0.000014"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acorr_ljungbox(huozai_df, lags=[6,12], boxpierce=True, return_df=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "db7cd2dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "saved...\n",
      "saved...\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,4),dpi=150, facecolor='whitesmoke')\n",
    "ax1 = fig.add_subplot(121)\n",
    "ACF(huozai_df, lag=24)\n",
    "ax2 = fig.add_subplot(122)\n",
    "PACF(huozai_df, lag=24)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "609a8ba8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                               SARIMAX Results                                \n",
      "==============================================================================\n",
      "Dep. Variable:                   fire   No. Observations:                   50\n",
      "Model:                 ARIMA(2, 0, 0)   Log Likelihood                 -75.622\n",
      "Date:                Fri, 07 Jun 2024   AIC                            159.245\n",
      "Time:                        21:05:48   BIC                            166.893\n",
      "Sample:                             0   HQIC                           162.157\n",
      "                                 - 50                                         \n",
      "Covariance Type:                  opg                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const       1000.5052      0.452   2213.637      0.000     999.619    1001.391\n",
      "ar.L1          1.1172      0.171      6.552      0.000       0.783       1.451\n",
      "ar.L2         -0.4778      0.152     -3.146      0.002      -0.775      -0.180\n",
      "sigma2         1.1731      0.231      5.069      0.000       0.720       1.627\n",
      "===================================================================================\n",
      "Ljung-Box (L1) (Q):                   0.27   Jarque-Bera (JB):                 1.32\n",
      "Prob(Q):                              0.61   Prob(JB):                         0.52\n",
      "Heteroskedasticity (H):               1.54   Skew:                             0.37\n",
      "Prob(H) (two-sided):                  0.38   Kurtosis:                         3.29\n",
      "===================================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.tsa.arima.model import ARIMA\n",
    "import warnings \n",
    "\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "huozai_est = ARIMA(huozai_df, order=(2,0,0)).fit()\n",
    "print(huozai_est.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a946bef4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lb_stat</th>\n",
       "      <th>lb_pvalue</th>\n",
       "      <th>bp_stat</th>\n",
       "      <th>bp_pvalue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.488526</td>\n",
       "      <td>0.745496</td>\n",
       "      <td>3.130784</td>\n",
       "      <td>0.792262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>8.294159</td>\n",
       "      <td>0.761741</td>\n",
       "      <td>7.015120</td>\n",
       "      <td>0.856613</td>\n",
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      ],
      "text/plain": [
       "     lb_stat  lb_pvalue   bp_stat  bp_pvalue\n",
       "6   3.488526   0.745496  3.130784   0.792262\n",
       "12  8.294159   0.761741  7.015120   0.856613"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "huozai_resid = huozai_est.resid\n",
    "acorr_ljungbox(huozai_resid, lags=[6,12],boxpierce=True, return_df=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "53b02e95",
   "metadata": {},
   "outputs": [],
   "source": []
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